Compositions and methods for robust dynamic metabolic control

ABSTRACT

The present disclosure provides compositions and methods for rapid production of chemicals in genetically engineered microorganisms in a large scale. Also provided herein is a high-throughput metabolic engineering platform enabling the rapid optimization of microbial production strains. The platform, which bridges a gap between current in vivo and in vitro bio-production approaches, relies on dynamic minimization of the active metabolic network.

CROSS-REFERENCE

This application is a continuation of U.S. application Ser. No. 16/487,542, filed Aug. 21, 2019 which is a National Stage Entry of PCT/US 18/19040, filed Feb. 21, 2018 which claims the benefit of U.S. Provisional Application No. 62/461,436, filed Feb. 21, 2017, which application is incorporated herein by reference in its entirety.

STATEMENT AS TO FEDERALLY SPONSORED RESEARCH

This invention was made with Government support under Federal Grant Nos. HR0011-14-C-0075, 12043956, and 1445726 awarded by the DOD/DARPA, NAVY/ONR, and NSF, respectively. The Government has certain rights to this invention.

REFERENCE TO A SEQUENCE LISTING

The instant application contains a Sequence Listing which has been filed electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Feb. 21, 2018, is named 52240_702_601_SL.txt and is 81,697 bytes in size.

BACKGROUND OF THE INVENTION

Biotechnology-based fermentation processes have been successfully developed to produce everything from biologics and small molecule therapies to specialty, bulk and commodity chemicals, and even next generation biofuels. These processes have made rapid advancements in recent years due to technology developments in the fields of fermentation science and synthetic biology, as well as metabolic and enzyme engineering. Despite these substantial advances, most successful examples of rational and directed engineering approaches have also greatly relied on numerous and often lengthy cycles of trial and error. The present disclosure provides a strategy that simultaneously reduces the complexity of the problem (as well as the size of the relevant design space), while also minimizing metabolic responses to environmental conditions, increasing robustness and scalability of engineered strains.

SUMMARY OF THE INVENTION

The present disclosure provides, in part, a high-throughput engineering platform that enables the rapid development of microbial production strains.

In one aspect, the present disclosure provides a cell for generating a product, wherein the cell comprises: a heterologous polynucleotide for controlled reduction of expression of an enzyme of a metabolic pathway, wherein the controlled reduction of expression of the enzyme induces a stationary phase of the cell; and a heterologous production polynucleotide for mediating controlled increase in expression of a production enzyme for generation of the product; wherein a rate of production of the product during the stationary phase is reduced less in response to a change of an environmental condition as compared to a cell lacking the enzyme.

In some embodiments, the heterologous polynucleotide reduces flux through the metabolic pathway. In some embodiments, the enzyme is selected from the group consisting of enoyl-ACP/CoA reductase, glucose-6-phosphate dehydrogenase, lipoamide dehydrogenase, citrate synthase, soluble transhydrogenase, and NADH-dependent glyceraldehyde-3-phosphate dehydrogenase. In some embodiments, the production enzyme is selected from the group consisting of NADPH-dependent alanine dehydrogenase, an alanine exporter, and NADPH-dependent glyceraldehyde-3-phosphate dehydrogenase. In some embodiments, the change of an environmental condition comprises increasing or decreasing a concentration of a sugar in a culture medium contacting the cell. In some embodiments, the sugar is glucose. In some embodiments, the change of an environmental condition comprises increasing or decreasing oxygenation of a culture medium contacting the cell. In some embodiments, the product comprises 3-hydroxypropionic acid.

In some embodiments, the product comprises an amino acid. In some aspects, the amino acid comprises alanine. In some aspects, the cell is grown in a culture, and a rate of production of the alanine by the culture is at least 0.5 g/L/hour. In some aspects, the rate of production of the alanine is at least 1.0 g/L/hour. In some aspects, the rate of production of the alanine is at least 1.5 g/L/hour. In some aspects, the rate of production of the alanine is at least 1.6 g/L/hour. In some aspects, the culture produces at least 80 g/L of the alanine. In some aspects, the culture produces at least 100 g/L of the alanine. In some aspects, the culture produces at least 120 g/L of the alanine. In some aspects, the culture produces at least 140 g/L of the alanine. In some aspects, the production polynucleotide encodes an alanine exporter. In some aspects, the alanine exporter is alaE.

In some embodiments, the product comprises mevalonic acid. In some embodiments, the cell is grown in a culture, and a rate of production of the mevalonic acid by the culture is at least 0.5 g/L/hour. In some embodiments, the rate of production of the mevalonic acid is at least 1.0 g/L/hour. In some embodiments, the rate of production of the mevalonic acid is at least 1.2 g/L/hour. In some embodiments, the rate of production of the mevalonic acid is at least 1.25 g/L/hour. In some aspects, the cell is grown in a culture, and the culture produces at least 50 g/L of the mevalonic acid. In some embodiments, the culture produces at least 70 g/L of the mevalonic acid. In some embodiments, the culture produces at least 90 g/L of the mevalonic acid. In some embodiments, the culture produces at least 95 g/L of the mevalonic acid. In some embodiments, the heterologous polynucleotide is selected from the group consisting of: a silencing polynucleotide for repressing transcription of a gene encoding the enzyme; and a degradation polynucleotide for mediating cellular degradation of the enzyme.

In some aspects, the heterologous polynucleotide comprises a silencing polynucleotide, and the silencing polynucleotide comprises a guide RNA (gRNA) comprising a gRNA sequence that recognizes a promoter of a gene encoding the enzyme. In some aspects, the heterologous polynucleotide encodes a CRISPR enzyme, and the CRISPR enzyme specifically binds to the promoter sequence when bound to the gRNA. In some aspects, the CRISPR enzyme is catalytically inactive. In some aspects, the heterologous polynucleotide comprises a degradation polynucleotide, wherein the degradation polynucleotide comprises a sequence encoding a degradation tag, wherein the degradation tag mediates degradation of the enzyme. In some embodiments, expression of the heterologous polynucleotide is regulated by phosphate availability in the cell. In some embodiments, expression of the production polynucleotide is regulated by phosphate availability in the cell. In some embodiments, the cell is an E. coli cell.

In another aspect, disclosed herein is a method comprising: culturing independently a plurality of strains of a cell, wherein each strain comprises (i) a heterologous polynucleotide for mediating controlled reduction of expression of an enzyme of a metabolic pathway, wherein the controlled reduction of expression of the enzyme induces a stationary phase of the cell; and (ii) a heterologous production polynucleotide for mediating controlled increase in expression of a production enzyme for generation of the product; wherein each strain of the plurality of strains differs from another strain in a sequence of at least one of the heterologous polynucleotide or the heterologous production polynucleotide; growing the plurality of strains to stationary phase; and selecting a strain of the plurality of strains based on a level of the product produced by the selected strain during the stationary phase.

In some embodiments, the method comprises determining the level of the product. In some embodiments, the method comprises growing the selected strain. In some embodiments, the selected strain is grown in a bioreactor. In some embodiments, a culture medium comprising the selected strain has a volume of at least 500 ml. In some embodiments, the culture medium has a volume of at least 1 L. In some embodiments, the heterologous polynucleotide is selected from the group consisting of: a silencing polynucleotide for repressing transcription of a gene encoding the enzyme; and a degradation polynucleotide for mediating cellular degradation of the enzyme. In some embodiments, a first and second strain of the plurality of strains comprises a silencing polynucleotide. In some embodiments, the silencing polynucleotide comprises a guide RNA (gRNA) comprising a gRNA sequence that recognizes a promoter sequence of a gene encoding the enzyme. In some embodiments, the gRNA sequence differs between the first and second strains. In some embodiments, the first and second strain of the plurality of strains comprise a degradation polynucleotide. In some embodiments, the degradation polynucleotide differs between the first and second strains. In some embodiments, the enzyme is selected from the group consisting of enoyl-ACP/CoA reductase, glucose-6-phosphate dehydrogenase, lipoamide dehydrogenase, citrate synthase, soluble transhydrogenase, and NADH-dependent glyceraldehyde-3-phosphate dehydrogenase. In some embodiments, the production enzyme is selected from the group consisting of NADPH-dependent alanine dehydrogenase, an alanine exporter, and NADPH-dependent glyceraldehyde-3-phosphate dehydrogenase. In some embodiments, the product is selected from the group consisting of mevalonic acid, 3-hydroxypropionic acid, and an amino acid.

In some embodiments, the product is an amino acid and the amino acid is alanine. In some embodiments, the cell of the selected strain a rate of production of the product during the stationary phase is reduced less in response to a change of an environmental condition as compared to a cell lacking the heterologous polynucleotide. In some embodiments, the change of an environmental condition comprises a change in concentration of a sugar of a culture medium contacting the cell. In some embodiments, the change of an environmental condition comprises a change in oxygenation of a culture medium contacting the cell.

In another aspect, disclosed herein is a method of generating a cellular product comprising: culturing a heterologous cell in a culture medium, wherein the heterologous cell comprises: (i) a heterologous polynucleotide for mediating controlled reduction of expression of an enzyme of a metabolic pathway, wherein the controlled reduction of expression of the enzyme induces a stationary phase of the cell; and (ii) a heterologous production polynucleotide for mediating controlled increase in expression of a production enzyme for generation of the product; wherein a rate of production of the product during the stationary phase is reduced less in response to a change of an environmental condition as compared to a cell lacking the enzyme.

In one embodiment, the method further comprises changing the environmental condition. In one embodiment, the environmental condition comprises a concentration of a sugar of the culture medium, and changing the environmental condition comprises increasing or decreasing the concentration. In some embodiments, the sugar is glucose. In some embodiments, the environmental condition comprises an oxygen concentration of the culture medium, and changing the environmental condition comprises increasing or decreasing the oxygen concentration. In some embodiments, the culturing is performed in a bioreactor. In some embodiments, the culture medium has a volume of at least 500 ml. In some embodiments, the culture medium has a volume of at least 1 L. In some embodiments, the product comprises 3-hydroxypropionic acid. In some embodiments, the product comprises an amino acid. In some embodiments, the amino acid comprises alanine. In some embodiments, the rate of production of the alanine is at least 0.5 g/L/hour. In some embodiments, the rate of production of the alanine is at least 1.0 g/L/hour. In some embodiments, the rate of production of the alanine is at least 1.5 g/L/hour. In some embodiments, the rate of production of the alanine is at least 1.6 g/L/hour. In some embodiments, the production polynucleotide encodes an alanine exporter. In some embodiments, the alanine exporter is alaE.

In some embodiments, the product comprises mevalonic acid. In some embodiments, the rate of production of the mevalonic acid is at least 0.5 g/L/hour. In some embodiments, the rate of production of the mevalonic acid is at least 1.0 g/L/hour. In some embodiments, the rate of production of the mevalonic acid is at least 1.2 g/L/hour. In some embodiments, the rate of production of the mevalonic acid is at least 1.25 g/L/hour. In some embodiments, the heterologous polynucleotide is selected from the group consisting of: a silencing polynucleotide for repressing transcription of a gene encoding the enzyme; and a degradation polynucleotide for mediating cellular degradation of the enzyme. In some embodiments, the heterologous polynucleotide comprises a silencing polynucleotide, and the silencing polynucleotide comprises a guide RNA (gRNA) comprising a gRNA sequence that recognizes a promoter sequence of a gene encoding the enzyme. In some embodiments, the heterologous polynucleotide encodes a CRISPR enzyme, wherein the CRISPR enzyme specifically binds to the promoter sequence when bound to the gRNA. In some embodiments, the CRISPR enzyme is catalytically inactive. In some embodiments, the heterologous polynucleotide comprises a degradation polynucleotide, wherein the degradation polynucleotide comprises a sequence encoding a degradation tag, wherein the degradation tag mediates degradation of the enzyme. In some embodiments, the expression of the heterologous polynucleotide is regulated by phosphate availability in the cell. In some embodiments, the expression of the production polynucleotide is regulated by phosphate availability in the cell. In some embodiments, the cell is an E. coli cell.

In another aspect, disclosed herein is a cell for production of alanine, wherein the cell comprises: (i) a heterologous polynucleotide for controlled reduction of expression of an enzyme of a metabolic pathway, wherein the enzyme is selected from the group consisting of enoyl-ACP/CoA reductase, glucose-6-phosphate dehydrogenase, lipoamide dehydrogenase (lpd), citrate synthase (gltA), soluble transhydrogenase, and NADH-dependent glyceraldehyde-3-phosphate dehydrogenase; and (ii) an alanine exporter, wherein the alanine exporter is expressed at increased levels as compared to a wildtype cell.

In some embodiments, the alanine exporter is encoded by an alaE gene. In some embodiments, the controlled reduction of expression of the enzyme induces a stationary phase of the cell. In some embodiments, the cell further comprises a heterologous production polynucleotide for controlled increase in expression of a production enzyme for generation of the alanine. In some embodiments, the production enzyme is selected from the group consisting of NADPH-dependent alanine dehydrogenase and NADPH-dependent glyceraldehyde-3-phosphate dehydrogenase. In some embodiments, the heterologous polynucleotide is selected from the group consisting of: a silencing polynucleotide for mediating transcriptional repression of a gene encoding the enzyme; and a degradation polynucleotide for mediating cellular degradation of the enzyme. In some embodiments, the heterologous polynucleotide comprises a silencing polynucleotide, and the silencing polynucleotide comprises a guide RNA (gRNA) comprising a gRNA sequence that recognizes a promoter sequence of a gene encoding the enzyme. In some embodiments, the polynucleotide further encodes a CRISPR enzyme, wherein the CRISPR enzyme specifically binds to the promoter sequence when bound to the gRNA. In some embodiments, the CRISPR enzyme is catalytically inactive. In some embodiments, the heterologous polynucleotide comprises a degradation polynucleotide, wherein the degradation polynucleotide comprises a sequence encoding a degradation tag, wherein the degradation tag mediates degradation of the enzyme. In some embodiments, the polynucleotide is regulated by phosphate availability in the cell. In some embodiments, the production polynucleotide is regulated by phosphate availability in the cell. In some embodiments, the cell is an E. coli cell.

In some embodiments, a culture comprises the cell. In some embodiments, a rate of production of the alanine by the culture is at least 0.5 g/L/hour. In some embodiments, a rate of production of the alanine by the culture is at least 1.0 g/L/hour. In some embodiments, a rate of production of the alanine by the culture is at least 1.5 g/L/hour. In some embodiments, a rate of production of the alanine by the culture is at least 1.6 g/L/hour. In some embodiments, the culture produces at least 100 g/L of the alanine. In some embodiments, the culture produces at least 120 g/L of the alanine. In some embodiments, the culture produces at least 140 g/L of the alanine.

In some aspects, disclosed herein is a method of production of alanine comprising growing in a culture medium a cell comprising (i) a heterologous polynucleotide for controlled reduction of expression of a enzyme of a metabolic pathway, wherein the enzyme is selected from the group consisting of enoyl-ACP/CoA reductase, glucose-6-phosphate dehydrogenase, lipoamide dehydrogenase, citrate synthase, soluble transhydrogenase, and NADH-dependent glyceraldehyde-3-phosphate dehydrogenase; and (ii) an alanine exporter, wherein the alanine exporter is expressed at increased levels as compared to a wildtype cell.

In some embodiments, the controlled reduction of expression of the enzyme induces a stationary phase of the cell. In some embodiments, the method further comprises decreasing an oxygenation level or a sugar concentration of the culture medium during the stationary phase, wherein a rate of production of the cellular product is reduced less in response to the decreasing as compared to a cell lacking the heterologous polynucleotide. In some embodiments, the sugar is glucose. In some embodiments, the alanine exporter is encoded by an alaE gene. In some embodiments, the cell further comprises a heterologous production polynucleotide for controlled increase in expression of a production enzyme for generation of the alanine. In some embodiments, the production enzyme is selected from the group consisting of: NADPH-dependent alanine dehydrogenase and NADPH-dependent glyceraldehyde-3-phosphate dehydrogenase. In some embodiments, the heterologous polynucleotide is selected from the group consisting of: a silencing polynucleotide for mediating transcriptional repression of a gene encoding the enzyme; and a degradation polynucleotide for mediating cellular degradation of the enzyme. In some embodiments, the heterologous polynucleotide comprises a silencing polynucleotide, and the silencing polynucleotide comprises a guide RNA (gRNA) comprising a gRNA sequence that recognizes a promoter sequence of a gene encoding the enzyme. In some embodiments, the heterologous polynucleotide encodes a CRISPR enzyme, wherein the CRISPR enzyme specifically binds to the promoter sequence when bound to the gRNA. In some embodiments, the CRISPR enzyme is catalytically inactive. In some embodiments, the heterologous polynucleotide comprises a degradation polynucleotide, wherein the degradation polynucleotide comprises a sequence encoding a degradation tag, wherein the degradation tag mediates degradation of the enzyme.

In some embodiments, the expression of the heterologous polynucleotide is regulated by phosphate availability in the cell. In some embodiments, the production polynucleotide is regulated by phosphate availability in the cell. In some embodiments, the cell is an E. coli cell. In some embodiments, a rate of production of the alanine is at least 0.5 g/L/hour. In some embodiments, a rate of production of the alanine is at least 1.0 g/L/hour. In some embodiments, a rate of production of the alanine is at least 1.5 g/L/hour. In some embodiments, a rate of production of the alanine is at least 1.6 g/L/hour.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

FIG. 1A depicts an overview of dynamic metabolic control in 2-stage fermentations.

FIG. 1B depicts strain and bioprocess optimization.

FIGS. 2A-D depict an example of implementation of 2-stage Synthetic Metabolic Valves (SMVs) in E. coli.

FIGS. 3A-K depict an example of alanine production in E. coli utilizing 2-stage dynamic control.

FIGS. 4A-F depict example robustness comparison between 2-stage and growth associated approaches.

FIGS. 5A-J depict example comparisons of “Valve” and growth associated alanine production in micro-fermentations and 1 L fermentation.

FIG. 6A-H depict an example of mevalonate production in E. coli utilizing 2-stage dynamic control.

FIG. 7 depicts an example of phosphate depletion promoter characterization.

FIG. 8 depicts an example of insulated phosphate depletion promoter characterization.

FIG. 9 depicts an example of insulated constitutive promoter characterization.

FIG. 10 depicts an example of metabolic modeling results for optimal 3-HP flux in two stage fermentations.

FIG. 11 depicts examples of chromosomal modifications.

FIG. 12 depicts an example of average maximal growth rates of starting host strains in 1 L FGM10 minimal medium fermentations, n=2.

FIG. 13A-E depict examples of distribution of glucose utilized during the growth phase of starting host strains in 1 L standard minimal medium fermentations.

FIG. 14 depicts pCASCADE-control plasmid construction scheme.

FIGS. 15A-B depict pCASCADE construction scheme.

FIGS. 16A-C depict an overview of micro-fermentation process.

FIG. 17 depicts micro-fermentation for L-alanine production using different insulated phosphate promoters in DLF_0025 strain.

FIG. 18 depicts Heatmap for L-alanine production by gapN/gapA strains.

FIGS. 19A-D depict alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 20A-D depict alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 21A-D depict alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 22A-D depict alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 23A-D depict alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 24A-D depict alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 25A-D depict alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 26A-D depict alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 27A-D depict alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 28A-D depict alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 29A-D depict alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 30A-D depict alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 31A-D depict alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIG. 32 depicts alanine production in response to different OTR and glucose concentration in micro-fermentation for one strain evaluated for robustness.

FIGS. 33A-B depict growth profile for all valve and growth associated strains at 1 L scale evaluated in this paper.

FIG. 34 depicts specific Productivity (SP) comparison for strain with highest mevalonate titer from literature and mevalonate strain 1 evaluated in this work.

FIG. 35 depicts alanine standard curve from MS measurement. Average and standard deviation for mass spec response from triplicate standard measurement were plotted.

FIGS. 36A-B depict glucose and ethanol standard curves from RI measurement.

FIG. 37 depicts 3-Hydroxypropionic acid standard curve from TUV measurement.

FIGS. 38A-D depict TUV standard curves for L-alanine, D-alanine, mevalonic acid, and mevalonolactone.

DETAILED DESCRIPTION OF THE INVENTION Definitions

As used in the specification and the claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to an “expression vector” includes a single expression vector as well as a plurality of expression vectors, either the same (e.g., the same operon) or different; reference to “microorganism” includes a single microorganism as well as a plurality of microorganisms; and the like.

As used herein, “reduced enzymatic activity,” “reducing enzymatic activity,” and the like is meant to indicate that a microorganism cell's, or an isolated enzyme, exhibits a lower level of activity than that measured in a comparable cell of the same species or its native enzyme. That is, enzymatic conversion of the indicated substrate(s) to indicated product(s) under known standard conditions for that enzyme is at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, or at least 90 percent less than the enzymatic activity for the same biochemical conversion by a native (non-modified) enzyme under a standard specified condition. This term also can include elimination of that enzymatic activity. A cell having reduced enzymatic activity of an enzyme can be identified using any method known in the art. For example, enzyme activity assays can be used to identify cells having reduced enzyme activity. See, for example, Enzyme Nomenclature, Academic Press, Inc., New York 2007.

The term “heterologous DNA,” “heterologous nucleic acid sequence,” and the like as used herein refers to a nucleic acid sequence wherein at least one of the following is true: (a) the sequence of nucleic acids foreign to (i.e., not naturally found in) a given host microorganism; (b) the sequence may be naturally found in a given host microorganism, but in an unnatural (e.g., greater than expected) amount; or (c) the sequence of nucleic acids comprises two or more subsequences that are not found in the same relationship to each other in nature. For example, regarding instance (c), a heterologous nucleic acid sequence that is recombinantly produced will have two or more sequences from unrelated genes arranged to make a new functional nucleic acid, such as a nonnative promoter driving gene expression.

The term “synthetic metabolic valve,” and the like as used herein refers to either the use of controlled proteolysis, gene silencing or the combination of both proteolysis and gene silencing to alter metabolic fluxes.

The term “heterologous” is intended to include the term “exogenous” as the latter term is generally used in the art. With reference to the host microorganism's genome prior to the introduction of a heterologous nucleic acid sequence, the nucleic acid sequence that codes for the enzyme is heterologous (whether or not the heterologous nucleic acid sequence is introduced into that genome).

As used herein, the term “gene disruption,” or grammatical equivalents thereof (and including “to disrupt enzymatic function,” “disruption of enzymatic function,” and the like), is intended to mean a genetic modification to a microorganism that renders the encoded gene product as having a reduced polypeptide activity compared with polypeptide activity in or from a microorganism cell not so modified. The genetic modification can be, for example, deletion of the entire gene, deletion or other modification of a regulatory sequence required for transcription or translation, deletion of a portion of the gene which results in a truncated gene product (e.g., enzyme) or by any of various mutation strategies that reduces activity (including reducing activities to no detectable activity level) the encoded gene product. A disruption may broadly include a deletion of all or part of the nucleic acid sequence encoding the enzyme, and also includes, but is not limited to other types of genetic modifications, e.g., introduction of stop codons, frame shift mutations, introduction or removal of portions of the gene, and introduction of a degradation signal, those genetic modifications affecting mRNA transcription levels and/or stability, and altering the promoter or repressor upstream of the gene encoding the enzyme.

Bio-production or fermentation, as used herein, may be aerobic, microaerobic, or anaerobic.

When the genetic modification of a gene product, e.g., an enzyme, is referred to herein, including the claims, it is understood that the genetic modification is of a nucleic acid sequence, such as or including the gene, that normally encodes the stated gene product, e.g., the enzyme.

As used herein, the term “metabolic flux” and the like refers to changes in metabolism that lead to changes in product and/or byproduct formation, including production rates, production titers and production yields from a given substrate.

Species and other phylogenic identifications are according to the classification known to a person skilled in the art of microbiology.

Enzymes are listed here within, with reference to a Universal Protein Resource (Uniprot) identification number, which would be well known to one skilled in the art (Uniprot is maintained by and available through the UniProt Consortium).

Where methods and steps described herein indicate certain events occurring in certain order, those of ordinary skill in the art will recognize that the ordering of certain steps may be modified and that such modifications are in accordance with the variations of the invention. Additionally, certain steps may be performed concurrently in a parallel process when possible, as well as performed sequentially.

The meaning of abbreviations is as follows: “C” means Celsius or degrees Celsius, as is clear from its usage, DCW means dry cell weight, “s” means second(s), “min” means minute(s), “h,” “hr,” or “hrs” means hour(s), “psi” means pounds per square inch, “nm” means nanometers, “d” means day(s), “L” or “uL” or “ul” means microliter(s), “mL” means milliliter(s), “L” means liter(s), “mm” means millimeter(s), “nm” means nanometers, “mM” means millimolar, “μM” or “uM” means micromolar, “M” means molar, “mmol” means millimole(s), “μmol” or “uMol” means micromole(s), “g” means gram(s), “μg” or “ug” means microgram(s) and “ng” means nanogram(s), “PCR” means polymerase chain reaction, “OD” means optical density, “OD₆₀₀” means the optical density measured at a photon wavelength of 600 nm, “kDa” means kilodaltons, “g” means the gravitation constant, “bp” means base pair(s), “kbp” means kilobase pair(s), “% w/v” means weight/volume percent, “% v/v” means volume/volume percent, “IPTG” means isopropyl-μ-D-thiogalactopyranoiside, “aTc” means anhydrotetracycline, “RBS” means ribosome binding site, “rpm” means revolutions per minute, “HPLC” means high performance liquid chromatography, and “GC” means gas chromatography.

Overview

Provided herein is a high-throughput metabolic engineering platform enabling the rapid optimization of microbial production strains. The platform, which bridges a gap between current in vivo and in vitro bio-production approaches, relies on dynamic minimization of the active metabolic network. Dynamic metabolic network minimization can be accomplished using combinations of CRISPR interference and controlled proteolysis to reduce the activity of multiple enzymes in essential central metabolism. Minimization can be implemented in the context of standardized 2-stage bio-processes. This approach not only can result in a design space with greatly reduced complexity, but also in increased metabolic fluxes and production rates as well as in strains which are robust to environmental conditions. Robustness can lead to predictable scalability from high-throughput small-scale screens, or “micro-fermentations”, to fully instrumented bioreactors. Predictive high-throughput approaches may be critical for metabolic engineering programs to truly take advantage of the rapidly increasing throughput and decreasing costs of synthetic biology. The examples provided herein have not only demonstrated proof of principle for this approach in the common industrial microbe: E. coli, and has validated this approach with the rapid optimization of E. coli strains producing two important industrial chemicals: alanine and mevalonic acid, at commercially meaningful rates, titers (147 g/L and 97 g/L, respectively), and yields.

Also provided herein are systems and methods to rapidly optimize a microorganism for chemical productions in a high-throughput fashion.

Also provided herein are microorganisms that can be used with the disclosed platform and/or methods for chemical productions.

Synthetic Metabolic Valves (SMVs)

The current disclosure describes the construction of synthetic metabolic valves (SMVs) comprising one or more or a combination of the following: controlled gene silencing and controlled proteolysis. It is appreciated that one well skilled in the art is aware of several methodologies for gene silencing and controlled proteolysis.

The development of platform microbial strains that utilize SMVs can decouple growth from product formation. These strains enable the dynamic control of metabolic pathways, including those that when altered have negative effects on microorganism growth. Dynamic control over metabolism is accomplished via a combination of methodologies including but not limited to transcriptional silencing and controlled enzyme proteolysis. These microbial strains are utilized in a multi-stage bioprocess encompassing as least two stages, the first stage in which microorganisms are grown and metabolism can be optimized for microbial growth and at least one other stage in which growth can be slowed or stopped, and dynamic changes can be made to metabolism to improve production of desired product, such as a chemical or fuel. The transition of growing cultures between stages and the manipulation of metabolic fluxes can be controlled by artificial chemical inducers or preferably by controlling the level of key limiting nutrients. In addition, genetic modifications may be made to provide metabolic pathways for the biosynthesis of one or more chemical or fuel products. Also, genetic modifications may be made to enable the utilization of a variety of carbon feedstocks including but not limited sugars such as glucose, sucrose, xylose, arabinose, mannose, and lactose, oils, carbon dioxide, carbon monoxide, methane, methanol and formaldehyde.

This approach allows for simpler models of metabolic fluxes and physiological demands during a production phase, turning a growing cell into a stationary phase biocatalyst. These synthetic metabolic valves can be used to turn off essential genes and redirect carbon, electrons and energy flux to product formation in a multi-stage fermentation process One or more of the following enables these synthetic valves: 1) transcriptional gene silencing or repression technologies in combination with 2) inducible enzyme degradation and 3) nutrient limitation to induce a stationary or non-dividing cellular state. SMVs are generalizable to any pathway and microbial host. These synthetic metabolic valves allow for novel rapid metabolic engineering strategies useful for the production of renewable chemicals and fuels and any product that can be produced via whole cell catalysis.

In various cases, one SMV can refer to the manipulation of one gene (or its protein product). The manipulation can be controlled silencing of the gene and/or controlled degradation of its protein product. In certain cases, combination of SMVs can lead to improved production in yields, rate and/or robustness, which includes manipulation of two genes (or their protein products). In some cases, an engineered microorganism comprises at least one SMV. In some cases, an engineered microorganism comprises more than one SMV. In some cases, an engineered microorganism comprises two, three, four, five, six, seven, eight, nine, or ten, or more SMVs.

Method and Systems for Bio-Production

Provided herein are methods or systems for robust large scale production of molecules from biologics and small molecule therapeutics to specialty, bulk and commodity chemicals, and biofuels. The methods or systems provided herein comprise using engineered microorganism which comprises a limited set of metabolic enzymes. In some embodiments, the engineered microorganism comprises at least one metabolic enzyme that has reduced level or activity. In some embodiments, the engineered microorganism comprises two, three, four, five, six, seven, eight, nine, or ten, or more metabolic enzymes that have reduced level or activity. The methods and systems provided herein can reduce metabolic responses to environmental conditions and can be easily transferred from small scale (e.g. mgs) production to large scale (e.g. kgs) production. The methods and systems provided herein can reduce the time and costs associated with transitioning from small scale (e.g. mgs) to large scale (e.g. kgs) production.

Within the scope of the current disclosure are genetically modified microorganism, wherein the microorganism is capable of producing a product derived from any key metabolic intermediate including but not limited to malonyl-CoA, pyruvate, oxaloacetate, erthyrose-4-phosphate, xylulose-5-phosphate, alpha-ketoglutarate and citrate at a specific rate selected from the rates of greater than 0.05 g/gDCW-hr, 0.08 g/gDCW-hr, greater than 0.1 g/gDCW-hr, greater than 0.13 g/gDCW-hr, greater than 0.15 g/gDCW-hr, greater than 0.175 g/gDCW-hr. greater than 0.2 g/gDCW-hr, greater than 0.25 g/gDCW-hr, greater than 0.3 g/gDCW-hr, greater than 0.35 g/gDCW-hr, greater than 0.4 g/gDCW-hr, greater than 0.45 g/gDCW-hr, or greater than 0.5 g/gDCW-hr.

In various embodiments, the invention includes a culture system comprising a carbon source in an aqueous medium and a genetically modified microorganism, wherein said genetically modified organism is present in an amount selected from greater than 0.05 gDCW/L, 0.1 gDCW/L, greater than 1 gDCW/L, greater than 5 gDCW/L, greater than 10 gDCW/L, greater than 15 gDCW/L, or greater than 20 gDCW/L, such as when the volume of the aqueous medium is selected from greater than 5 mL, greater than 100 mL, greater than 0.5 L, greater than 1 L, greater than 2 L, greater than 10 L, greater than 250 L, greater than 1000 L, greater than 10,000 L, greater than 50,000 L, greater than 100,000 L or greater than 200,000 L, and such as when the volume of the aqueous medium is greater than 250 L and contained within a steel vessel.

Carbon Sources

Bio-production media, which is used in the present invention with recombinant microorganisms must contain suitable carbon sources or substrates for both growth and production stages. Suitable substrates may include, but are not limited to glucose, sucrose, xylose, mannose, arabinose, oils, carbon dioxide, carbon monoxide, methane, methanol, formaldehyde and glycerol. It is contemplated that all of the above mentioned carbon substrates and mixtures thereof are suitable in the present invention as a carbon source(s).

Microorganisms

Features as described and claimed herein may be provided in a microorganism selected from the listing herein, or another suitable microorganism, that also comprises one or more natural, introduced, or enhanced product bio-production pathways. Thus, in some embodiments the microorganism(s) comprise an endogenous product production pathway (which may, in some such embodiments, be enhanced), whereas in other embodiments the microorganism does not comprise an endogenous product production pathway.

The examples describe specific modifications and evaluations to certain bacterial and fungal microorganisms. The scope of the invention is not meant to be limited to such species, but to be generally applicable to a wide range of suitable microorganisms.

Suitable host cells or host microorganisms for bio-production can be either prokaryotic or eukaryotic. Suitable host cells or host microorganisms can be bacteria such as Citrobacter, Enterobacter, Clostridium, Klebsiella, Aerobacter, Lactobacillus, Aspergillus, Saccharomyces, Schizosaccharomyces, Zygosaccharomyces, Pichia, Kluyveromyces, Candida, Hansenula, Debaryomyces, Mucor, Torulopsis, Methylobacter, Escherichia, Salmonella, Bacillus, Streptomyces, and Pseudomonas. In some embodiments, a host cell or an engineered cell is E. coli. In some embodiments, a host cell or an engineered cell is S. cerevisiae.

In certain aspects, provided herein is a microorganism genetically modified to comprise: a production pathway comprising at least one enzyme for the biosynthesis of a product, and a combination of multiple synthetic metabolic valves to controllably reduce or eliminate flux through multiple metabolic pathways. In some embodiments, each of the multiple synthetic metabolic valves comprises one or more genes for (i) controlled silencing of gene expression of at least one gene or (ii) the controlled proteolytic inactivation of at least one protein. In some embodiments, a rate of the biosynthesis of the product is increased in a productive stationary phase upon a depletion of a nutrient, wherein the depletion of the nutrient induces the multiple synthetic metabolic valves. In some cases, the controlled silencing of gene expression is accomplished by RNA interference, CRISPR interference or transcriptional repression. In some cases, the controlled proteolytic inactivation is accomplished by protein cleavage by a specific protease or targeted degradation by specific peptide tags. In some cases, the nutrient is phosphate, nitrogen, sulfur, magnesium, or a combination thereof.

In certain aspects, provided herein is a genetically modified microorganism comprising: a production pathway comprising at least one enzyme for the biosynthesis of a product from one of the following metabolites: pyruvate, acetolactate, acetyl-CoA, acetoacetyl-CoA or malonyl-CoA; and a combination of multiple synthetic metabolic valves, wherein each of the multiple synthetic metabolic valves comprises one of a fabI, gltA, lpd, zwf or udhA gene for (i) controlled silencing of gene expression of a corresponding one of said fabI, gltA, lpd, zwf or udhA genes or (ii) controlled proteolytic inactivation of a protein encoded by a corresponding one of said fabI, gltA, lpd, zwf or udhA genes. In some embodiments, a rate of the biosynthesis of the product is increased in a productive stationary phase upon a depletion of a nutrient, wherein the depletion of the nutrient induces the multiple synthetic metabolic valves. In some embodiments, the product is alanine or a derivative thereof. In some embodiments, the product is mevalonate or a derivative thereof. In some embodiments, the product is malonic acid or a derivative thereof. In some embodiments, the nutrient is phosphate, nitrogen, sulfur, magnesium, or a combination thereof.

In certain aspects, provided herein is a genetically modified microorganism comprising: a production pathway to produce alanine from pyruvate; and a combination of multiple synthetic metabolic valves, wherein each of the multiple synthetic metabolic valves comprises one of a fabI, gltA, lpd, zwf or udhA gene for (i) controlled silencing of gene expression of a corresponding one of said fabI, gltA, lpd, zwf or udhA genes or (ii) controlled proteolytic inactivation of a protein encoded by one of said fabI, gltA, lpd, zwf or udhA genes. In some embodiments, a rate of the biosynthesis of alanine is increased in a productive stationary phase upon a depletion of a nutrient, wherein the depletion of the nutrient induces the multiple synthetic metabolic valves. In some embodiments, the nutrient is phosphate, nitrogen, sulfur, magnesium, or a combination thereof.

In some cases, a genetically modified microorganism is a heterologous cell. In some cases, provided herein is a heterologous cell for generating a product. In some cases, a heterologous cell comprises an engineered valve polynucleotide for mediating controlled reduction of expression of a valve enzyme acting in a metabolic pathway. In certain cases, a controlled reduction of expression of a valve enzyme reduces flux through a metabolic pathway, wherein the controlled reduction of expression of the valve enzyme induces a stationary phase of the heterologous cell. In some cases, a heterologous cell further comprises an engineered production polynucleotide for mediating controlled increase in expression of a production enzyme for generation of the product. In some situations, a heterologous cell comprises an engineered valve polynucleotide for mediating controlled reduction of expression of a valve enzyme acting in a metabolic pathway, wherein a rate of production of a product during a stationary phase is reduced less in response to a change of an environmental condition as compared to a cell lacking the controlled reduction of expression of the valve enzyme.

In some cases, provided herein is a heterologous cell for generating a product, wherein said cell comprises: an engineered valve polynucleotide for mediating controlled reduction of expression of a valve enzyme acting in a metabolic pathway, wherein said controlled reduction of expression of said valve enzyme reduces flux through said metabolic pathway, wherein said controlled reduction of expression of said valve enzyme induces a stationary phase of said cell; and an engineered production polynucleotide for mediating controlled increase in expression of a production enzyme for generation of said product, wherein a rate of production of said product during said stationary phase is reduced less in response to a change of an environmental condition as compared to a cell lacking said controlled reduction of expression of said valve enzyme.

In some cases, provided herein is a cell comprising a reduced expression or activity of a valve enzyme, wherein the valve enzyme comprises an enzyme selected from the group consisting of enoyl-ACP/CoA reductase (fabI), glucose-6-phosphate dehydrogenase (zwf), lipoamide dehydrogenase (lpd), citrate synthase (gltA), soluble transhydrogenase (udhA). NADH-dependent glyceraldehyde-3-phosphate dehydrogenase (gapA), and a combination thereof.

In some cases, provided herein is a cell comprising a production enzyme, wherein the production enzyme comprises an enzyme selected from the group consisting of NADPH-dependent alanine dehydrogenase (ald), alanine exporter (alaE), NADPH-dependent glyceraldehyde-3-phosphate dehydrogenase (gapN), and a combination thereof.

Environmental Conditions

Environmental conditions can comprise medium and culture conditions. Environmental factors that may influence production can be temperature, pH, acidity, ethanol, sulfite, and availability of nutrients.

In addition to an appropriate carbon source, such as selected from one of the herein disclosed types, bio-production media may contain suitable minerals, salts, cofactors, buffers and other components, known to those skilled in the art, suitable for the growth of the cultures and promotion of the enzymatic pathway necessary for chemical product bio-production under the present disclosure. Another aspect of the invention regards media and culture conditions that comprise genetically modified microorganisms of the invention and optionally supplements.

Typically cells are grown at a temperature in the range of about 25° C. to about 40° C. in an appropriate medium, as well as up to 70° C. for thermophilic microorganisms. Suitable growth media are well characterized and known in the art.

Suitable pH ranges for the bio-production are between pH 2.0 to pH 10.0, where pH 6.0 to pH 8.0 is a typical pH range for the initial condition. However, the actual culture conditions for a particular embodiment are not meant to be limited by these pH ranges.

Bio-productions may be performed under aerobic, microaerobic or anaerobic conditions with or without agitation.

In some cases, a change of an environmental condition comprises a change in sugar concentration of a culture medium contacting a cell. In some cases, a change in sugar concentration of a culture medium is an increase of sugar concentration. In some other cases, a change in sugar concentration is a decrease of sugar concentration. In some situations, an increase of sugar concentration is from 1% to 2%, from 2% to 3%, from 3% to 4%, from 4% to 5%, from 5% to 10%, from 10% to 15%, from 15% to 20%, from 20% to 30%, from 30% to 40%, from 40% to 50%, from 50% to 60%, from 60% to 70%, from 70% to 80%, from 80% to 90%, or from 90% to 100% more sugar compared with the original sugar concentration in the culture medium. In some situations, a decrease of sugar concentration is from 1% to 2%, from 2% to 3%, from 3% to 4%, from 4% to 5%, from 5% to 10%, from 10% to 15%, from 15% to 20%, from 20% to 30%, from 30% to 40%, from 40% to 50%, from 50/o to 60%, from 60% to 70%, from 70% to 80%, from 80% to 90%, or from 90% to 100% less sugar compared with the original sugar concentration in the culture medium.

In some cases, a change of an environmental condition comprises a change in oxygenation of a culture medium contacting a cell. In some cases, a change in oxygenation of a culture medium is an increase of oxygenation. In some other cases, a change in oxygenation of a culture medium is a decrease of oxygenation. In some situations, an increase of oxygenation is the addition of oxygen from 1% to 2%, from 2% to 3%, from 3% to 4%, from 4% to 5%, from 5% to 10%, from 10% to 15%, from 15% to 20%, from 20% to 30%, from 30% to 40%, from 40% to 50%, from 50% to 60%, from 60% to 70%, from 70% to 80%, from 80% to 90%, or from 90% to 100% more than the original amount of oxygen added in a culture medium. In some situations, a decrease of oxygenation is the addition of oxygen from 1% to 2%, from 2% to 3%, from 3% to 4%, from 4% to 5%, from 5% to 10%, from 10% to 15%, from 15% to 20%, from 20% to 30%, from 30% to 40%, from 40% to 50%, from 50% to 60%, from 60% to 70%, from 70% to 80%, from 80% to 90%, or from 90% to 100% less than the original amount of oxygen added in a culture medium.

Bio-Production Reactors and Systems

Fermentation systems utilizing methods and/or compositions according to the invention are also within the scope of the invention.

Any of the recombinant microorganisms as described and/or referred to herein may be introduced into an industrial bio-production system where the microorganisms convert a carbon source into a product in a commercially viable operation. The bio-production system includes the introduction of such a recombinant microorganism into a bioreactor vessel, with a carbon source substrate and bio-production media suitable for growing the recombinant microorganism, and maintaining the bio-production system within a suitable temperature range (and dissolved oxygen concentration range if the reaction is aerobic or microaerobic) for a suitable time to obtain a desired conversion of a portion of the substrate molecules to a selected chemical product. Bio-productions may be performed under aerobic, microaerobic, or anaerobic conditions, with or without agitation. Industrial bio-production systems and their operation are well-known to those skilled in the arts of chemical engineering and bioprocess engineering. The amount of a product produced in a bio-production media generally can be determined using a number of methods known in the art, for example, high performance liquid chromatography (HPLC), gas chromatography (GC) or GC/Mass Spectroscopy (MS).

Genetic Modifications, Nucleotide Sequences, and Amino Acid Sequences

Embodiments of the present disclosure may result from introduction of an expression vector into a host microorganism, wherein the expression vector contains a nucleic acid sequence coding for an enzyme that is, or is not, normally found in a host microorganism.

The ability to genetically modify a host cell is essential for the production of any genetically modified (recombinant) microorganism. The mode of gene transfer technology may be by electroporation, conjugation, transduction, or natural transformation. A broad range of host conjugative plasmids and drug resistance markers are available. The cloning vectors are tailored to the host organisms based on the nature of antibiotic resistance markers that can function in that host. Also, as disclosed herein, a genetically modified (recombinant) microorganism may comprise modifications other than via plasmid introduction, including modifications to its genomic DNA.

More generally, nucleic acid constructs can be prepared comprising an isolated polynucleotide encoding a polypeptide having enzyme activity operably linked to one or more (several) control sequences that direct the expression of the coding sequence in a microorganism, such as E. coli, under conditions compatible with the control sequences. The isolated polynucleotide may be manipulated to provide for expression of the polypeptide. Manipulation of the polynucleotide's sequence prior to its insertion into a vector may be desirable or necessary depending on the expression vector. The techniques for modifying polynucleotide sequences utilizing recombinant DNA methods are well established in the art.

The control sequence may be an appropriate promoter sequence, a nucleotide sequence that is recognized by a host cell for expression of a polynucleotide encoding a polypeptide of the present disclosure. The promoter sequence may contain transcriptional control sequences that mediate the expression of the polypeptide. The promoter may be any nucleotide sequence that shows transcriptional activity in the host cell of choice including mutant, truncated, and hybrid promoters, and may be obtained from genes encoding extracellular or intracellular polypeptides either homologous or heterologous to the host cell. The techniques for modifying and utilizing recombinant DNA promoter sequences are well established in the art.

For various embodiments of the invention the genetic manipulations may be described to include various genetic manipulations, including those directed to change regulation of, and therefore ultimate activity of, an enzyme or enzymatic activity of an enzyme identified in any of the respective pathways. Such genetic modifications may be directed to transcriptional, translational, and post-translational modifications that result in a change of enzyme activity and/or selectivity under selected and/or identified culture conditions and/or to provision of additional nucleic acid sequences such as to increase copy number and/or mutants of an enzyme related to product production. Specific methodologies and approaches to achieve such genetic modification are well known to one skilled in the art.

In various embodiments, to function more efficiently, a microorganism may comprise one or more gene deletions. For example, in E. coli, the genes encoding the lactate dehydrogenase (IdhA), phosphate acetyltransferase (pta), pyruvate oxidase (poxB), pyruvateformate lyase (pflB), methylglyoxal synthase (mgsA), acetate kinase (ackA), alcohol dehydrogenase (adhE), the clpXP protease specificity enhancing factor (sspB), the ATPdependent Lon protease (Ion), the outer membrane protease (ompT), the arcA transcriptional dual regulator (arcA), and the iclR transcriptional regulator (iclR) may be disrupted, including deleted. Such gene disruptions, including deletions, are not meant to be limiting, and may be implemented in various combinations in various embodiments. Gene deletions may be accomplished by numerous strategies well known in the art, as are methods to incorporate foreign DNA into a host chromosome.

In various embodiments, to function more efficiently, a microorganism may comprise one or more synthetic metabolic valves, composed of enzymes targeted for controlled proteolysis, expression silencing or a combination of both controlled proteolysis and expression silencing. In some embodiments, a microorganism may comprise two, three, four, five, six, seven, eight, nine, or ten, or more synthetic metabolic valves. For example, one enzyme encoded by one gene or a combination of numerous enzymes encoded by numerous genes in E. coli may be designed as synthetic metabolic valves to alter metabolism and improve product formation. Representative genes in E. coli may include but are not limited to the following: fabI, zwf gltA, ppc, udhA, lpd, sucD, aceA, pfkA, Ion, rpoS, tktA or tktB. It is appreciated that it is well known to one skilled in the art how to identify homologues of these genes and or other genes in additional microbial species.

For all nucleic acid and amino acid sequences provided herein, it is appreciated that conservatively modified variants of these sequences are included, and are within the scope of the invention in its various embodiments. Functionally equivalent nucleic acid and amino acid sequences (functional variants), which may include conservatively modified variants as well as more extensively varied sequences, which are well within the skill of the person of ordinary skill in the art, and microorganisms comprising these, also are within the scope of various embodiments of the invention, as are methods and systems comprising such sequences and/or microorganisms.

Accordingly, as described in various sections above, some compositions, methods and systems of the present disclosure comprise providing a genetically modified microorganism that comprises both a production pathway to make a desired product from a central intermediate in combination with synthetic metabolic valves to redistribute flux.

Aspects of the invention also regard provision of multiple genetic modifications to improve microorganism overall effectiveness in converting a selected carbon source into a selected product. Particular combinations are shown, such as in the Examples, to increase specific productivity, volumetric productivity, titer and yield substantially over more basic combinations of genetic modifications. In addition to the above-described genetic modifications, in various embodiments genetic modifications, including synthetic metabolic valves also are provided to increase the pool and availability of the cofactor NADPH and/or NADH which may be consumed in the production of a product.

More generally, and depending on the particular metabolic pathways of a microorganism selected for genetic modification, any subgroup of genetic modifications may be made to decrease cellular production of fermentation product(s) other than the desired fermentation product, selected from the group consisting of acetate, acetoin, acetone, acrylic, malate, fatty acid ethyl esters, isoprenoids, glycerol, ethylene glycol, ethylene, propylene butylene, isobutylene, ethyl acetate, vinyl acetate, other acetates, 1,4-butanediol, 2,3-butanediol, butanol, isobutanol, sec-butanol, butyrate, isobutyrate, 2-OH-isobutryate, 3-OHbutyrate, ethanol, isopropanol, D-lactate, L-lactate, pyruvate, itaconate, levulinate, glucarate, glutarate, caprolactam, adipic acid, propanol, isopropanol, fusel alcohols, and 1,2-propanediol, 1,3-propanediol, formate, fumaric acid, propionic acid, succinic acid, valeric acid, maleic acid and poly-hydroxybutyrate. Gene deletions may be made as disclosed generally herein, and other approaches may also be used to achieve a desired decreased cellular production of selected fermentation products other than the desired products. VLA Gene Silencing

In particular the invention describes the use of controlled gene silencing to help enable the control over metabolic fluxes in controlled multi-stage fermentation processes. There are several methodologies known in the art for controlled gene silencing, including but not limited to mRNA silencing or RNA interference, silencing via transcriptional repressors and CRISPR interference.

In some cases, a valve polynucleotide comprises a polynucleotide selected from the group consisting of: a silencing polynucleotide for repressing transcription of a gene encoding said valve enzyme; a degradation polynucleotide for mediating cellular degradation of said valve enzyme, and a combination thereof.

In some cases, a valve polynucleotide comprises a silencing polynucleotide, and said silencing polynucleotide comprises a guide RNA (gRNA) comprising a gRNA sequence that recognizes a promoter of a gene encoding said valve enzyme.

In some cases, a valve polynucleotide further encodes a CRISPR enzyme, wherein said CRISPR enzyme specifically binds to said promoter sequence when bound to said gRNA. In some cases, a CRISPR enzyme is catalytically inactive.

In some cases, a valve polynucleotide comprises a degradation polynucleotide, wherein said degradation polynucleotide comprises a sequence encoding a degradation tag, wherein said degradation tag mediates degradation of said valve enzyme. In some cases, the expression of a valve polynucleotide is regulated by phosphate availability in a cell. In some cases, the expression of a production polynucleotide is regulated by phosphate availability in a cell. In certain cases, the cell is an E. coli cell.

Controlled Proteolysis

In particular the current disclosure describes the use of controlled protein degradation or proteolysis to help enable the control over metabolic fluxes in controlled multi-stage fermentation processes. There are several methodologies known in the art for controlled protein degradation, including but not limited to targeted protein cleavage by a specific protease and controlled targeting of proteins for degradation by specific peptide tags. Systems for the use of the E. coli clpXP protease for controlled protein degradation can be used. This methodology relies upon adding a specific C-terminal peptide tag such as a DAS4 (or DAS+4) tag. Proteins with this tag are not degraded by the clpXP protease until the specificity enhancing chaperone sspB is expressed. sspB induces degradation of DAS4 tagged proteins by the clpXP protease. In additional numerous site specific protease systems are well known in the art. Proteins can be engineered to contain a specific target site of a given protease and then cleaved after the controlled expression of the protease. In some embodiments the cleavage can be expected lead to protein inactivation or degradation. For example, an N-terminal sequence can be added to a protein of interest to enable clpS dependent clpAP degradation. In addition, this sequence can further be masked by an additional N-terminal sequence, which can be controllable cleaved such as by a ULP hydrolase. This allows for controlled N-rule degradation dependent on hydrolase expression. It is therefore possible to tag proteins for controlled proteolysis either at the N-terminus or C-terminus.

The preference of using an N-terminal vs. C-terminal tag will largely depend on whether either tag affects protein function prior to the controlled onset of degradation. The invention describes the use of controlled protein degradation or proteolysis to help enable the control over metabolic fluxes in controlled multi-stage fermentation processes, in E. coli. There are several methodologies known in the art for controlled protein degradation in other microbial hosts, including a wide range of gram-negative as well as gram-positive bacteria, yeast and even archaea. In particular, systems for controlled proteolysis can be transferred from a native microbial host and used in a non-native host.

Synthetic Metabolic Valve Control

In particular the current disclosure describes the use of synthetic metabolic valves to control metabolic fluxes in multi-stage fermentation processes. There are numerous methodologies known in the art to induce expression that can be used at the transition between stages in multistage fermentations. These include but are not limited to artificial chemical inducers including: tetracycline, anhydrotetracycline, lactose, IPTG (isopropyl-beta-D-l-thiogalactopyranoside), arabinose, raffinose, tryptophan and numerous others. Systems linking the use of these well known inducers to the control of gene expression silencing and/or controlled proteolysis can be integrated into genetically modified microbial systems to control the transition between growth and production phases in multi-stage fermentation processes.

In addition, it may be desirable to control the transition between growth and production in multi-stage fermentations by the depletion of one or more limiting nutrients that are consumed during growth. Limiting nutrients can include but are not limited to: phosphate, nitrogen, sulfur and magnesium. Natural gene expression systems that respond to these nutrient limitations can be used to operably link the control of gene expression silencing and/or controlled proteolysis to the transition between growth and production phases in multi-stage fermentation processes.

Products

In some embodiments, provided herein is a microorganism or a cell for producing a product. In some cases, the product comprises 3-hydroxypropionic acid. In some cases, the product comprises an amino acid. In some cases, the amino acid comprises alanine. In some cases, the alanine is L-alanine. In some cases, the alanine is D-alanine. In some cases, a rate of production of alanine is at least 0.1 g/L/hr, 0.2 g/L/hr, 0.3 g/L/hr, 0.4 g/L/hr, 0.5 g/L/hr, 0.6 g/L/hr, 0.7 g/L/hr, 0.8 g/L/hr, 0.9 g/L/hr, 1.0 g/L/hr, 1.1 g/L/hr, 1.2 g/L/hr, 1.3 g/L/hr, 1.4 g/L/hr, 1.5 g/L/hr, 1.6 g/L/hr, 1.7 g/L/hr, 1.8 g/L/hr, 1.9 g/L/hr, 2.0 g/L/hr, 2.5 g/L/hr, 3.0 g/L/hr, 3.5 g/L/hr, 4.0 g/L/hr, 4.5 g/L/hr, 5.0 g/L/hr, 5.5 g/L/hr, 6.0 g/L/hr, 7.0 g/L/hr, 8.0 g/L/hr, 9.0 g/L/hr, or at least 10 g/L/hr.

In some cases, the alanine titers after 24 hours can be from 0 to 0.5 g/L, 0.5 g/L to 1 g/L, 1 g/L to 1.5 g/L, 1.5 g/L to 2 g/L, 2 g/L to 2.5 g/L, 2.5 g/L to 3 g/L, 3 g/L to 3.5 g/L, 3.5 g/L to 4 g/L, 4 g/L to 4.5 g/L, 4.5 g/L to S g/L, or from 5 g/L to 10 g/L. The dynamic range of alanine production offered by SMVs can be up to a 4-fold increase compared to that offered by solely altering the expression level of the production pathway enzymes (by changing the promoter). In some cases, the dynamic range of alanine production offered by SMVs can be up to a 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, or 10-fold increase compared to that offered by solely altering the expression level of the production pathway enzymes.

In some cases, a production polynucleotide in the microorganism encodes an alanine exporter. In some cases, the alanine exporter is alaE.

In some cases, the product comprises mevalonic acid. In some cases, a rate of production of mevalonic acid is at least 0.1 g/L/hr, 0.2 g/L/hr, 0.3 g/L/hr, 0.4 g/L/hr, 0.5 g/L/hr, 0.6 g/L/hr, 0.7 g/L/hr, 0.8 g/L/hr, 0.9 g/L/hr, 1.0 g/L/hr, 1.1 g/L/hr, 1.2 g/L/hr, 1.3 g/L/hr, 1.4 g/L/hr, 1.5 g/L/hr, 1.6 g/L/hr, 1.7 g/L/hr, 1.8 g/L/hr, 1.9 g/L/hr, 2.0 g/L/hr, 2.5 g/L/hr, 3.0 g/L/hr, 3.5 g/L/hr, 4.0 g/L/hr, 4.5 g/L/hr, 5.0 g/L/hr, 5.5 g/L/hr, 6.0 g/L/hr, 7.0 g/L/hr, 8.0 g/L/hr, 9.0 g/L/hr, or at least 10 g/L/hr.

Methods

Provided herein are methods for producing a product in an engineered microorganism in a large scale. Also provided herein are methods for engineering microorganisms for large-scale production of a product in a high-throughput fashion.

In some cases, provided herein is a method, comprising: culturing a plurality of strains of a cell, wherein each strain of said plurality of strains comprises (i) an engineered valve polynucleotide for mediating controlled reduction of expression of a valve enzyme acting in a metabolic pathway, wherein said controlled reduction of expression of said valve enzyme reduces flux through said metabolic pathway; and (ii) an engineered production polynucleotide for mediating controlled increase in expression of a production enzyme for generation of said product; wherein each strain of said plurality of strains differs from another strain in a sequence of at least one of said engineered valve polynucleotide or said engineered production polynucleotide; measuring a level of said product generated by each of said plurality of strains; and selecting a strain based on said level of said product. In some embodiments, the method further comprises growing said selected strain in a bioreactor. In some embodiments, a culture medium comprising said selected strain has a volume of at least 100 ml, 200 ml, 300 ml, 400 ml, 500 ml, 600 ml, 700 ml, 800 ml, 900 ml, or at least 1000 ml. In some embodiments, a culture medium has a volume of at least 1 L.

In some embodiments, a valve polynucleotide comprises a polynucleotide selected from the group consisting of: a silencing polynucleotide for repressing transcription of a gene encoding said valve enzyme; a degradation polynucleotide for mediating cellular degradation of said valve enzyme; and a combination thereof. In some embodiments, a first and a second strain of said plurality of strains comprise a silencing polynucleotide. In some embodiments, a silencing polynucleotide comprises a guide RNA (gRNA) comprising a gRNA sequence that recognizes a promoter of a gene encoding said valve enzyme. In some embodiments, a gRNA sequence differs between said first and second strains. In some embodiments, a promoter recognized by said gRNA differs between said first and second strains. In some embodiments, a first strain comprises said silencing polynucleotide and said degradation polynucleotide, and a second strain comprises said silencing polynucleotide but does not comprise said degradation polynucleotide. In some embodiments, a level of product is greater in said second strain than said first strain. In some embodiments, a level of product is greater in said first strain than said second strain. In some embodiments, a valve enzyme comprises an enzyme selected from the group consisting of enoyl-ACP/CoA reductase (fabI), glucose-6-phosphate dehydrogenase (zwf), lipoamide dehydrogenase (lpd), citrate synthase (gltA), soluble transhydrogenase (udhA), NADPH-dependent glyceraldehyde-3-phosphate dehydrogenase (gapA), and a combination thereof. In some embodiments, a production enzyme comprises an enzyme selected from the group consisting of NADPH-dependent alanine dehydrogenase (ald), alanine exporter (alaE), NADPH-dependent glyceraldehyde-3-phosphate dehydrogenase (gapN), and a combination thereof.

In some embodiments, a product is selected from the group consisting of mevalonic acid, 3-hydroxypropionic acid, an amino acid, and a combination thereof. In some embodiments, the amino acid is alanine. In some embodiments, the alanine is L-alanine. In some embodiments, the alanine is D-alanine.

In some embodiments, a rate of production of the product during said stationary phase is reduced less in response to a change of an environmental condition as compared to a cell lacking said controlled reduction of expression of said valve enzyme.

In some embodiments, a change of an environmental condition comprises a change in a sugar concentration of a culture medium contacting said cell.

In some embodiments, a change of an environmental condition comprises a change in oxygenation of a culture medium contacting said cell.

In some cases, provided herein is a method of generating a cellular product comprising: culturing a heterologous cell in a culture medium, wherein said heterologous cell comprises: (i) an engineered valve polynucleotide for mediating controlled reduction of expression of a valve enzyme acting in a metabolic pathway, wherein said controlled reduction of expression of said valve enzyme reduces flux through said metabolic pathway, wherein said controlled reduction of expression of said valve enzyme induces a stationary phase of said cell; and (ii) an engineered production polynucleotide for mediating controlled increase in expression of a production enzyme for generation of said product; wherein a rate of production of said product during said stationary phase is reduced less in response to a change of an environmental condition as compared to a cell lacking said controlled reduction of expression of said valve enzyme. In some embodiments, the method further comprises changing said environmental condition. In some embodiments, the environmental condition comprises a sugar concentration of said culture medium, and changing said environmental condition comprises increasing or decreasing said sugar concentration. In some cases, said sugar is glucose, sucrose, lactose, maltose, xylose, mannitol, or a combination thereof. In some cases, said sugar is glucose. In some cases, the environmental condition comprises an oxygen concentration of said culture medium, and changing said environmental condition comprises increasing or decreasing said oxygen concentration. In some cases, said culturing is performed in a bioreactor.

In some cases, said culture medium has a volume of at least 100 ml, 200 ml, 300 ml, 400 ml, 500 ml, 600 ml, 700 ml, 800 ml, 900 ml, or at least 1000. In some cases, said culture medium has a volume of at least 1 L. In some case, said product comprises 3-hydroxypropionic acid. In some cases, said product comprises an amino acid. In some cases, said amino acid comprises alanine.

In some cases, a rate of production of said alanine is at least 0.1 g/L/hr, 0.2 g/L/hr, 0.3 g/L/hr, 0.4 g/L/hr, 0.5 g/L/hr, 0.6 g/L/hr, 0.7 g/L/hr, 0.8 g/L/hr, 0.9 g/L/hr, 1.0 g/L/hr, 1.1 g/L/hr, 1.2 g/L/hr, 1.3 g/L/hr, 1.4 g/L/hr, 1.5 g/L/hr, 1.6 g/L/hr, 1.7 g/L/hr, 1.8 g/L/hr, 1.9 g/L/hr, 2.0 g/L/hr, 2.5 g/L/hr, 3.0 g/L/hr, 3.5 g/L/hr, 4.0 g/L/hr, 4.5 g/L/hr, 5.0 g/L/hr, 5.5 g/L/hr, 6.0 g/L/hr, 7.0 g/L/hr, 8.0 g/L/hr, 9.0 g/L/hr. or at least 10 g/L/hr. In some cases, said production polynucleotide encodes an alanine exporter. In some cases, said alanine exporter is alaE. In some cases, said culturing occurs for less than 20 hours, 30 hours, 40 hours, 50 hours, 60 hours, 70 hours, 80 hours, 90 hours, or less than 100 hours. In some cases, said culturing occurs for less than 10 hours, 15 hours, 20 hours, 25 hours, 30 hours, 35 hours, 40 hours, or less than 45 hours. In some cases, said culturing occurs for less than 30 hours.

In some cases, said product comprises mevalonic acid. In some cases, a rate of production of said mevalonic acid is at least 0.1 g/L/hr, 0.2 g/L/hr, 0.3 g/L/hr, 0.4 g/L/hr, 0.5 g/L/hr, 0.6 g/L/hr, 0.7 g/L/hr, 0.8 g/L/hr, 0.9 g/L/hr, 1.0 g/L/hr, 1.1 g/L/hr, 1.2 g/L/hr, 1.3 g/L/hr, 1.4 g/L/hr, 1.5 g/L/hr, 1.6 g/L/hr, 1.7 g/L/hr, 1.8 g/L/hr, 1.9 g/L/hr, 2.0 g/L/hr, 2.5 g/L/hr, 3.0 g/L/hr, 3.5 g/L/hr, 4.0 g/L/hr, 4.5 g/L/hr, 5.0 g/hr, 5.5 g/L/hr, 6.0 g/L/hr, 7.0 g/L/hr, 8.0 g/L/hr, 9.0 g/L/hr, or at least 10 g/L/hr. In some cases, said culturing occurs for less than 20 hours, 30 hours, 40 hours, 50 hours, 60 hours, 70 hours, 80 hours, 90 hours, or less than 100 hours. In some cases, said culturing occurs for less than 80 hours.

In some embodiments, a valve polynucleotide comprises a polynucleotide selected from the group consisting of: a silencing polynucleotide for repressing transcription of a gene encoding said valve enzyme; a degradation polynucleotide for mediating cellular degradation of said valve enzyme; and a combination thereof. In some cases, a valve polynucleotide comprises a silencing polynucleotide, and said silencing polynucleotide comprises a guide RNA (gRNA) comprising a gRNA sequence that recognizes a promoter of a gene encoding said valve enzyme. In some cases, a valve polynucleotide further encodes a CRISPR enzyme, wherein said CRISPR enzyme specifically binds to said promoter sequence when bound to said gRNA. In some cases, a CRISPR enzyme is catalytically inactive. In some case, a valve polynucleotide comprises a degradation polynucleotide, wherein said degradation polynucleotide comprises a sequence encoding a degradation tag, wherein said degradation tag mediates degradation of said valve enzyme. In some cases, an expression of said valve polynucleotide is regulated by phosphate. In some cases, an expression of said production polynucleotide is regulated by phosphate. In some cases, said cell is an E. coli cell.

Optimization of Bio-Production

Biotechnology based fermentation processes have been successfully developed to produce everything from biologics and small molecule therapeutics to specialty, bulk and commodity chemicals, and even next generation biofuels¹⁻³. These processes have made rapid advancements in recent years due to numerous technology developments^(4,5). It has never been easier to produce new molecules using synthetic biology. Despite these advances, a major challenge remains in taking molecules from proof of concept (POC) to commercially meaningful levels. Strain optimization, or overcoming the “mg” to “kg” hurdle has remained a key barrier to the successful commercialization of bio-processes. After the demonstration of POC, successful bio-process development routinely requires lengthy iterations of both microbial strain and fermentation optimization⁶⁻⁸ (FIG. 1B). These optimization efforts are often specific to the product or host strain of interest. The throughput of synthetic biology has outpaced that of metabolic engineering, partly due to a lack of broadly useful tools to perform meaningful and standardized optimization of engineered microbial strains in a high-throughput manner⁹.

There are numerous challenges in strain optimization and moving past POC levels, not the least of which are the size and complexity of the potential design space. In contrast to simpler gene circuits, amenable to electrical circuit models¹⁰⁻¹², metabolic networks are highly interconnected. Each metabolite and/or enzyme can interact with endless others. This combinatorial complexity results in a huge potential design space, which is intractable to the kinds of systematic experimentation required for the development of standardized design principles (Supplemental Materials, Table 1). The challenges in addressing such a large design space have persisted despite the dramatic advances in, and decreased costs of, reading and writing DNA that have led to new high-throughput DNA assembly and microbial strain construction methods¹³⁻¹⁶. It is not surprising that new synthetic biology technologies involving strain engineering are often demonstrated with easily screened or selected phenotypes^(13,17-19). Most of these are limited to a focus on optimizing a limited set of pathway specific enzymes.

One approach to overcome the complexity of this challenge is the use of in vitro systems for bio-production, which comprise a limited set of metabolic enzymes. However, these approaches have challenges in replicating key advantages of in vivo systems, including cofactor recycling and energy generation^(20, 21). Another approach to deal with this complexity is to develop faster screening methods for strain evaluation²². However, increased throughput alone can never evaluate the full complexity of the potential design space. In addition, results obtained from high-throughput studies often do not translate, even in the same microbe, to a different environment^(20,23,24). Small scale screens do not readily translate to larger scale production processes, leading to iterations of process optimization on top of strain optimization (FIG. 1B). This is because metabolism is highly regulated and can respond, sometimes dramatically, to changes in environmental conditions^(25,20,26-28). A lack of environmental robustness is traditionally one factor making the scale up of fermentation based processes difficult. This issue has led to the development of specialized complex micro-reactor systems for scale down offering only modest improvements in throughput^(20,29-31).

There remains a significant need for broadly applicable, rapid and robust approaches to greatly reduce the time and costs transitioning from “mgs” to “kgs”. Ideally, approaches should be amenable to multiple products and production hosts. Provided herein is the development of a generalizable, high-throughput strain optimization approach that enables the use of truly scalable, standardized fermentation processes. This approach, as outlined in FIG. 1B, panel b, involves the dynamic minimization of the active metabolic network³², which combines the benefits of a smaller design space common to in vitro approaches while maintaining the benefits of in vivo biosynthesis²⁰. We can isolate and focus on the minimal metabolic networks required for production. Utilizing combinations of synthetic metabolic valves (SMVs)^(32,33) (FIGS. 2A-D) we can dynamically minimize the metabolic network and redirect metabolic flux in the context of a standardized 2-stage fermentation process²⁰.

This approach can reduce the complexity of the problem and the size of the relevant design space, greatly speeding up optimization. In various embodiments, it is demonstrated herein that dynamic metabolic network minimization can improve pathway fluxes beyond those achievable with production pathway modifications alone (FIGS. 3A-K and 6A-H). Simultaneously, we demonstrate that dynamic network minimization reduces metabolic responses to environmental conditions, which increases the robustness and scalability of engineered strains (FIGS. 3A-K and 5A-J).

EXAMPLES

2-Stage Synthetic Metabolic Valves in E. coli

We first developed improved synthetic metabolic valves (SMVs) in E. coli that are capable of the dynamic reduction of protein levels in a 2-stage process. These SMVs can be used to reduce levels of key metabolic enzymes (or reduce enzymatic activities of key metabolic enzymes) and rely on controlled proteolysis or CRISPR-based gene silencing or both proteolysis and silencing in combination (FIGS. 2A-D)³²⁻³⁵. Cell growth and dynamic metabolic control can be implemented using phosphate depletion as an environmental trigger. Phosphate can be an ideal candidate as a trigger, as one of the costliest components of minimal media. In addition, stationary phases induced in E. coli by phosphate depletion have retained glycolytic uptake as well as increased protein expression^(31,36). Numerous promoter systems responding to phosphate are well characterized in E. coli as well as other microbes including S. cerevisiae ³⁷. Phosphate responsive promoter variants were evaluated (Supplemental Materials, Section 1) and subsequently used for 2-stage control.

SMVs were implemented in E. coli using the native Type I-E Cascade CRISPR system for induced gene silencing^(34,38), while controlled proteolysis was induced by incorporating C-terminal degron tags on target proteins, both as previously demonstrated^(63,33) (FIG. 2A). These systems were introduced into a host strain initially engineered for minimal byproduct formation and high biomass yields and growth rates (E. coli strain DLF_0025, Supplemental Materials, Section 3)^(24,27,28,39). Using this approach, as FIGS. 2A-D demonstrate, protein levels can be controlled in 2-stage processes, as exemplified by turning “ON” GFPuv and “OFF” mCherry fluorescent proteins with phosphate depletion in minimal medium. The combination of gene silencing with proteolysis results in the largest rates of protein degradation (FIGS. 2C-D). The specific impact of gene silencing and proteolysis on decay rates will likely vary depending on the host, target gene/enzyme, and its specific natural turnover rates and expression levels^(40,41).

Metabolic Network Minimization Leads to Improved Fluxes

With the successful demonstration of dynamic control of protein levels in a 2-stage process, we turned to investigate the dynamic control of metabolic fluxes in E. coli through controlled reduction of key central metabolic enzymes alone and in combination. Reducing fluxes through thermodynamically favored “committed” reactions in the network is expected to lead to increases in network metabolite pools (Supplemental Materials Section 5), and as a result, changes in pathway fluxes. Enzymes in key committed steps in central metabolic pathways were identified and chosen as initial SMV targets and alanine was chosen as an initial test product (FIGS. 3A-K). A set of strains were constructed for alanine production (FIG. 3A), comprising an NADPH-dependent alanine dehydrogenase (ald*)⁴². Variants with multiple combinations of SMVs in central metabolic enzymes were made, with either modifications to induce proteolysis or gene silencing or both in combination. (Supplemental Materials, Section 3). Together the set of strains having SMVs evaluated in 2-stage processes are identified as “Valve” strains. A panel of alanine “Valve” strains (˜500 strains in total) were evaluated for alanine production in standardized, 2-stage, 96-well plate based micro-fermentations (Supplemental Materials, Section 7). Alanine titers after 24 hours of production are given in FIGS. 3B-C. Briefly, alanine titers after 24 hours ranged from ˜0 g/L, to ˜4.7 g/L, and as expected, varied significantly with respect to the number and combination of SMVs; most SMV combinations lead to improved performance when compared to the control with no SMVs and the alanine pathway alone. In some cases, the alanine titers after 24 hours can be from 0 to 0.5 g/L, 0.5 g/L to 1 g/L, 1 g/L to 1.5 g/L, 1.5 g/L to 2 g/L, 2 g/L to 2.5 g/L, 2.5 g/L to 3 g/L, 3 g/L to 3.5 g/L, 3.5 g/L to 4 g/L, 4 g/L to 4.5 g/L, 4.5 g/L to 5 g/L, or from 5 g/L to 10 g/L. The dynamic range of alanine production offered by SMVs can be up to a 4-fold increase compared to that offered by solely altering the expression level of the production pathway enzymes (by changing the promoter) (Supplemental Materials, Section 7). In some cases, the dynamic range of alanine production offered by SMVs can be up to a 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, or 10-fold increase compared to that offered by solely altering the expression level of the production pathway enzymes. Importantly, the use of proteolysis or silencing alone and/or in combination had significant impacts on production, indicating that for each enzyme the fine tuning of activity using SMVs is critical. One of the best performing strains from the micro-fermentations was then evaluated in a minimal medium, 2-stage, 1 L fermentation with 10 gdcw/L of biomass (FIG. 3F), which resulted in 80 g/L 100% L-alanine after 48 hours of production with a yield of 0.8 g/g. Further engineering of this strain by overexpressing an alanine exporter (encoded by the E. coli alaE gene⁴³) resulted in 147 g/L 100% L-alanine after 27 hours of production with a yield within error of theoretical yield ˜1 g/g, (FIG. 3G).

Micro-Fermentation Robustness

A central hypothesis was that by restricting metabolism in the production stage, strain performance could not only be improved, but would be more robust to environmental (process) conditions. Simply put, carbon flow is restricted through a minimized metabolic network, which can no longer adapt via cellular responses to the environment. To test this hypothesis, strains were evaluated under different “micro-fermentation” process conditions. Glucose concentration and oxygen transfer rate (key process variables impacting strain performance in traditional fermentations²⁶) were varied (FIG. 3D, Supplemental Materials, Section 8), and alanine production measured. A robustness score (RS) was developed to quantify environmental robustness. Larger RS scores indicate more robust strains. Whereas relative standard deviation (RSD) is one metric for robustness, we wanted to incorporate a stricter measure of robustness which also incorporates the maximal deviation (Max Dev) a strain has under all process conditions (RS, Equation (1)).

$\begin{matrix} {{RS} = {100 - {\frac{{{average}({RSD})} + {\max ({Dev})}}{2}*100}}} & {{Equation}\mspace{14mu} (1)} \end{matrix}$

Robustness scores for a subset of 48 alanine “Valve” strains are given in FIG. 3E. Results from these experiments studies are tabulated in Supplemental Materials, Section 8. A Chi² analysis using a cutoff of RS>0.6 for robustness was used to identify key SMVs which statistically contribute to process robustness. The proteolytic degradation of fabI was a primary contributor to robustness (Chi²=13.85, P_(value)<0.001) and as a result, “Valve” strains with proteolytic degradation of fabI were used in further studies. In addition, the “Valve” strains with proteolytic degradation of gltA and/or the combination of the proteolytic degradation of fabI and gltA were found to also be significant contributors of robustness, albeit with a large P_(value).

2-Stage “Valve” Strains Compared to Traditional Growth Associated Strains

To compare the 2-stage approach enabled by SMVs to more traditional growth associated processes, we constructed 5 strains, with constitutively expressed alanine dehydrogenase (ald*), capable of the growth associated production of alanine. These growth associated strains varied in the strength of the promoter used to drive ald* expression⁴⁴ (Supplemental Materials, Section 2), yet utilized the same common no-valve control host strain. FIG. 5 illustrates the results of a direct comparison of “Valve” strains in a 2-stage process compared to “Growth Associated (GA)” strains in a traditional fermentation at the microtiter (FIGS. 5A-D) and 1 L (FIGS. 5E-J) scales. In micro-fermentations, 2-stage “Valve” strains outperformed GA strains with respect to titer and process robustness. The most robust GA strain from the micro-fermentation analysis (also with the highest production level) was compared to a robust “Valve” strain in 1 L fermentations with varied process conditions. The “Valve” strains showed consistent performance in all process conditions evaluated (FIG. 5E), consistent with results from micro-fermentations, where the GA strain had significant performance variability dependent on process. We hypothesized that the increased environmental robustness observed in both “micro-” and 1 L scale fermentations for “Valve” strains would lead to predictable scale up, where strains with improved performance in high-throughput micro-fermentations would reliably have improved performance in controlled bioreactors. To evaluate the scalability of the system, “Valve” alanine strains with statistically differentiated performance in micro-fermentations (P-value<0.001) were evaluated in standardized 2-stage 1 L fermentations and compared to all GA strains. Statistically different performances observed in “micro-fermentations” have scaled predictably to 1 L fermentations for 2-stage “Valve” strains. This contrasts with results obtained with GA strains where no correlation between micro-fermentation and 1 L performance was observed (FIGS. 5G-H).

Product Flxihbility

With the successful and predictable scale-up of alanine strains into 1 L fully instrumented fermentations, we moved to validate the technology platform for an additional product: mevalonic acid. To this end, additional dynamic production pathways were constructed for mevalonic acid biosynthesis (FIG. 6A). A set of two-gene production pathway plasmids encoding three enzymatic functions was constructed for mevalonic acid production, consisting of the E. faecalis mvaE and mvaS genes encoding a bifunctional acetyl-CoA acetyltransferase, NADPH dependent HMG-CoA reductase, and HMG-CoA synthase respectively. A mutant mvaS gene, mvaS(A110G) with higher activity was used^(45,46). Production plasmids were initially evaluated for mevalonate production in the control strain (FIG. 6B). The best producing plasmid was then introduced into a variety of engineered “Valve” strains and evaluated in micro-fermentations (FIG. 6C). A subset of statistically differentiated strains were then evaluated in 1 L fermentations to assess scalability (FIG. 6D), which, as in the case of alanine, was predictive. In some cases, a performing strain produced meaningful titers and yields, 97 g/L in 78 hrs of production with a yield of 0.46 g/g (84% of theoretical yield) (FIG. 6E). Specific productivity for this mevalonate strain is over 4-fold higher than the best previously reported results⁴⁷ (Supplemental Materials, Section 9).

DISCUSSION

Historically some of the most successful efforts to metabolically engineer the production of small molecules have leveraged the power of anaerobic metabolism to couple product formation with growth. This has allowed for the classical design and selection of industrial strains to produce many products including ethanol, succinic acid, lactate and isobutanol, which have leveraged the power of evolution and selection to reach optimal metabolic fluxes in engineered networks^(48,49). While growth associated production is not strictly linked to anaerobic metabolism, growth association greatly limits the number and variety of different molecules that can be made using synthetic biology. A generic, robust and accessible non-growth associated platform would greatly simplify the optimization and scale up of a diverse number of products.

In contrast to most existing 2-stage processes, which have relied on natural metabolic responses to environmental triggers for production improvement, we have taken the next step in actively minimizing the essential metabolic network and redirecting metabolites to products of interest. Many of the targeted essential central metabolic pathways in this work have traditionally been off limits to engineering strategies, as deleting essential enzymes is incompatible with growth and growth associated production in traditional fermentation. The dynamically minimized metabolic network also results in enhanced robustness to environmental variables enabling the faithful translation of high-throughput small-scale studies to larger instrumented fermentations. A current paradigm in the field is to improve the throughput of relevant strain evaluations by developing small-scale, custom-designed micro-reactors for enhanced process control. In contrast, our approach is a move in a new direction involving engineering microbial metabolism to be less sensitive to process changes, simplifying high-throughput experimentation.

Beyond robustness, we have demonstrated that combinatorial modifications to essential enzymes in minimal metabolic networks can lead to significant improvements in production, particularly when compared to altering production pathway expression levels alone. These large variations in performance are due to changes in a limited subset of key central metabolic nodes, likely resulting in altered metabolite levels. Compared to previous approaches to dynamically control enzyme levels, we demonstrate improved potential for fine tuning of protein levels with a combination of gene silencing and proteolysis⁵⁰. As stationary phase cells cannot dilute existing proteins with cell division, this dual approach makes sense. The specific control of the level of any given enzyme will of course also depend on natural turnover mechanisms. At first glance, it may still be surprising that the combination of both gene silencing and proteolysis together does not always result in improved performance, i.e. “more is not always better”. Future efforts may be needed to explain these results, which could either be due to a requirement of maintaining minimal fluxes in the larger network or a consequence of changes in the levels of key regulatory metabolites that are not part of the minimal network, yet influence network activity.

While the approach as demonstrated can address many issues common to most bio-production processes, many product specific challenges remain. The toxicity of a product or pathway metabolite may limit titers or production rates. A minimal network that may be optimal at a low titer, may not be optimal at elevated titers. In addition, the engineering of improved enzymes is often a challenge in many “mg” to “kg” projects.

Feasibility of adapting this approach to other microbial hosts is expected. Key requirements for new hosts include a rapid and robust growth phase, the ability to engineer dynamic control over protein levels, and a metabolically active stationary phase. Numerous microbes have well characterized nutrient triggers for productive stationary phase metabolism³⁶, for example nitrogen limitation in Ralstonia species, Yarrowia species and others⁵¹ ⁵². Even when these requirements are not naturally met, they can be engineered into the host such as S. cerevisiae or other microbes, with each potential host presenting unique challenges and corresponding solutions.

Future efforts can be aimed at applying this platform for molecules with more complex production pathways. This approach can offer a tractable route for rapid optimization to metabolic engineers and synthetic biologists, who wish to move past POC levels and begin to tackle problems at more industrially relevant rates, titers and yields.

Methods Reagents and Media

Unless otherwise stated, all materials and reagents were of the highest grade possible and purchased from Sigma (St. Louis, Mo.). C13 labeled Alanine (2,3-13C2, 99%) (Item # CLM-2734-PK) was purchased from Cambridge Isotope Laboratories, Inc. (Tewksbury, Mass.). Luria Broth was used for routine strain and plasmid propagation and construction. Working antibiotic concentrations were as follows: ampicillin (100 μg/mL), kanamycin (35 μg/mL), chloramphenicol (35 μg/mL), spectinomycin (100 μg/mL), zeocin (50 μg/mL), gentamicin (10 μg/mL), blasticidin (100 μg/mL), puromycin (150 μg/mL), tetracycline (5 μg/mL). Luria broth with low salt (Lennox formulation) was used to select for zeocin, blasticidin and puromycin resistant clones. In addition, for puromycin selection, phosphate buffer (pH=8.0) was added to LB Lennox to a final concentration of 50 mM. Media formulations including stock solutions are described in Supplemental Materials, Section 7.

E. coli Strain Construction

Oligonucleotides and synthetic linear DNA (Gblocks™) used for strain construction and confirmation are all given in Supplemental Materials, Section 3, and they were obtained from Integrated DNA Technologies (IDT, Coralville, Iowa). Strain BW25113 was obtained from the Yale Genetic Stock Center (CGSC http://cgsc.biology.yale.edu/). Strain BWapldf was a kind gift from George Chen (Tsinghua University)⁶². Chromosomal modifications were made using standard recombineering methodologies⁶³ either with direct antibiotic cassette integration in the case of C-terminal DAS+4 tags carrying antibiotic resistance cassettes, or through scarless tet-sacB selection and counterselection, strictly following the protocols of Li et al⁶⁴. The recombineering plasmid pSIM5 and the tet-sacB selection/counterselection marker cassette were kind gifts from Donald Court (NCI, https://redrecombineering.ncifcrf.gov/court-lab.html). Briefly, the tet-sacB selection/counterselection cassette was amplified using the appropriate oligos supplying ˜50 bp flanking homology sequences using Econotaq (Lucigen Middleton, Wis.) according to manufacturer's instructions, with an initial 10 minutes denaturation at 94° C., followed by 35 cycles of 94° C., for 15 seconds, 52° C. for 15 seconds, and 72° C. for 5 minutes. Cassettes used for “curing” of the tet-sacB cassette or direct integration (when an antibiotic marker is present) were obtained as gBlocks from IDT. In the case of the sspB gene deletion, the open reading frame deletion replaced with a kanamycin resistance was amplified from the Keio Collection strain, JW3197-1⁶⁵, and moved to the appropriate background strain using standard methodologies. The kanamycin resistance cassette was cured using the pCP20 plasmid, leaving an frt scar^(63,65). Chromosomal modifications were confirmed by PCR amplification and sequencing (Eton Biosciences) using paired oligonucleotides, either flanking the entire region, or in the case of DAS+4 tag insertions an oligo 5′ of the insertion and one internal to the resistance cassette.

E. coli Plasmid Construction

Primers used for the design and construction of CASCADE guides arrays were listed in Supplemental Materials, Section 6. Gene silencing guide arrays were expressed from a series of pCASCADE plasmids. The pCASCADE-control plasmid was prepared by swapping the pTet promoter in pcrRNA.Tet⁷³ with an insulated low phosphate induced ugpB promoter⁷⁴. Promoter sequences for all genes were obtained from EcoCyc database (https://ecocyc.orgf). In order to design CASCADE guide array, CASCADE PAM sites near the −35 or −10 box of the promoter of interest were identified, 30 bp at the 3′ end of PAM site was selected as the guide sequence and cloned into pCASCADE plasmid using Q5 site-directed mutagenesis (NEB, MA) following manufacturer's protocol, with the modification that 5% v/v DMSO was added to the Q5 PCR reaction. PCR cycles were as follows: amplification involved an initial denaturation step at 98° C. for 30 second followed by cycling at 98° C. for 10 second, 72° C. for 30 second, and 72° C. for 1.5 min (the extension rate was 30 second/kb) for 25 cycles, then a final extension for 2 min at 72° C. 2 μL of PCR mixture was used for 10 μL KLD reaction, which proceeded under room temperature for 1 hour, after which, 1 μL KLD mixture was used for electroporation.

The pCASCADE guide array plasmids were prepared by sequentially amplifying complementary halves of each smaller guide plasmid by PCR, followed by subsequent DNA assembly. The pCASCADE-control vector was used as template. pCASCADE plasmids with arrays of two or more guides were prepared using Q5 High-Fidelity 2X Master Mix (NEB, MA). PCR cycles were as follows: amplification involved an initial denaturation step at 98° C. for 30 second followed by cycling at 98° C. for 10 second, 66° C. for 30 second, and 72° C. for 45 second (the extension rate was 30 second/kb) for 35 cycles, then a final extension for 2 min at 72° C. PCR product was purified by gel-extraction, 20 μL ultrapure water was used to elute 50 μL PCR reaction purification. 1 μL of each eluted PCR product was used for 10 μL of Gibson Assembly (NEB, MA), which was completed by incubation at 50° C. for 15 min. 1 μL Gibson Assembly mix was used for electroporation.

Production pathways enzymes were expressed from high copy plasmids via low phosphate inducible promoters. Production pathway gene sequences were codon optimized using the Codon Optimization Tool from the IDT website, phosphorylated G-blocks™ were designed and purchased from IDT for each pathway. Plasmids were assembled using NEBuilder® HiFi DNA Assembly Master Mix following manufacturer's protocol (NEB, MA). pSMART-HC-Kan (Lucigen, WI) was used as backbone for all pathway plasmids. All plasmid sequences were confirmed by DNA sequencing (Eton Bioscience, NC) and deposited with Addgene.

E. coli BioLector

Single colonies of each strain were inoculated into 5 mL LB with appropriate antibiotics and cultured at 37° C., 220 rpm for 9 hours or until OD600 reached >2. 500 μL of the culture was inoculated into 10 mL SM10 medium with appropriate antibiotics, and cultured in a square shake flask (CAT #: 25-212, Genesee Scientific, Inc. San Diego, Calif.) at 37° C., 220 rpm for 16 hours. Cells were pelleted by centrifugation and the culture density was normalized to OD600=5 using FGM3 media. Growth and fluorescence measurements were obtained in a Biolector (m2p labs, Baesweiler, Germany) using a high mass transfer FlowerPlate (CAT #: MTP-48-B, m2p-labs. Germany). 40 μL of the OD normalized culture was inoculated into 760 μL of FGM3 medium with appropriate antibiotics. Biolector settings were as follows: RFP gain=100, GFP gain=20, Biomass gain=20, shaking speed=1300 rpm, temperature=37° C., humidity=85%. Every strain was analyzed in triplicate.

E. coli Micro-Fermentations

Plasmids were transformed into host strains by electroporation using ECM 630 High Throughput Electroporation System (Harvard Apparatus, Inc. Holliston, Mass.) following manufacturer's protocol or using individual electroporation cuvettes. Glycerol stocks were prepared for each transformation plate by adding equal volume of sterile 20% glycerol, and 3 μL were used to inoculate overnight culture in 150 μL SM10++ medium with appropriate antibiotics. Plates were covered with sandwich covers (Model # CR1596 obtained from EnzyScreen, Haarlam, The Netherlands). These covers ensured minimal evaporative loss during incubation. Unless otherwise stated, 96 well plates were cultured at 37° C., 400 rpm for 16 hours, shaker orbit is 25 mm. This combination of orbit and minimal shaking speed is required to obtain needed mass transfer coefficient and enable adequate culture oxygenation.

After 16 hours of growth, cells were pelleted by centrifugation, excess media was removed and cells were resuspended in 150 μL of FGM3 Wash solution. Subsequently cells were once again pelleted and again excess media was removed, pellet was resuspended in 50 μL, FGM3 No Phosphate media containing appropriate antibiotics. 5 μL of the resuspended culture was added to 195 μL of water for OD600 measurement using standard flat bottom 96 well plate. OD600 for production was normalized to OD600=1, using FGM3 No Phosphate media containing appropriate antibiotics, in a total volume of 150 μL using standard 96 well plate. Plates were covered with sandwich covers (Model # CR1596 obtained from EnzyScreen, Haarlam, The Netherlands) and 96 well plate cultures were incubated at 37° C., 400 rpm for 24 hours. After 24 hours of production, all samples from each well were pelleted by centrifugation and the supernatant collected for subsequent analytical measurement. Triplicate micro-fermentations were performed for each strain.

For growth associated alanine micro-fermentations, glycerol stock preparation and 16 hour overnight culture in SM10++ proceeded as described above. After 16 hours of growth in SM10++ medium, 5 μL of overnight culture was inoculated into 150 μL FGM3 with 40 mM phosphate containing appropriate antibiotic. Plates were covered with sandwich covers (Model # CR1596 obtained from EnzyScreen, Haarlam, The Netherlands) and 96 well plate cultures were incubated at 37° C., 400 rpm for 24 hours. After 24 hours of production, OD600 was recorded, all samples from each well were then pelleted by centrifugation and the supernatant collected for subsequent analytical measurement. Triplicate micro-fermentations were performed for each strain.

Micro-fermentation robustness evaluations were conducted as described in Supplemental Materials, Section 8.

1 L Fermentation Seeds

Single colony from transformation plate was inoculated into 5 ml. LB with appropriate antibiotics and cultured at 37° C., 220 rpm for 16 hours. 500 μL of the LB culture was inoculated into 50 mL SM10 media with appropriate antibiotics in square shake flask (CAT #: 25-214, Genesee Scientific, Inc. San Diego, Calif.), the culture was incubated at 37° C. with a shaking speed of 220 rpm for 24 hours, at which time OD600 is usually between 3 and 10, the culture was harvested by centrifugation at 4000 rpm for 15 min, supernatant was discarded and cell culture was normalized to OD600=10 using SM10 media. For 1 L fermentation seed, 6 mL of normalized OD600=10 culture was added to 1.5 mL of 50% glycerol in cryovials, and stored at −80° C.

1 L Fermentations

An Infors-HT Multifors (Laurel, Md., USA) parallel bioreactor system was used to perform 1 L fermentations, including three gas connection mass flow controllers configured for air, oxygen and nitrogen gases. Vessels used had a total volume of 1400 mL and a working volume of up to 1 L. Online pH and pO2 monitoring and control were accomplished with Hamilton probes. Offgas analysis was accomplished with a multiplexed Blue-in-One BlueSens gas analyzer (BlueSens. Northbrook, Ill., USA). Culture densities were continually monitored using Optek 225 mm OD probes, (Optek, Germantown, Wis., USA). The system used was running IrisV6.0 command and control software and integrated with a Seg-flow automated sampling system (Flownamics, Rodeo, Calif., USA), including FISP cell free sampling probes, a Segmod 4800 and FlowFraction 96 well plate fraction collector.

For the standardized 2-stage process with ˜10 gcdw/L biomass, tanks were filled with 800 mL of FGM10 medium, with enough phosphate to target a final E. coli biomass concentration ˜10 gcdw/L. Antibiotics were added as appropriate. Frozen seed vials were thawed on ice and 7.5 mL of seed culture was used to inoculate the tanks. After inoculation, tanks were controlled at 37° C. and pH 6.8 using 5 M ammonium hydroxide and 1 M hydrochloric acid as titrants. 10 M ammonium hydroxide was used for FIG. 3G fermentation run. The following oxygen control scheme was used to maintain the desired dissolved oxygen set point. First gas flow rate was increased from a minimum of 0.3 L/min of air to 0.8 L/min of air, subsequently, if more aeration was needed, agitation was increased from a minimum of 300 rpm to a maximum of 1000 rpm. Finally, if more oxygen was required to achieve the set point, oxygen supplementation was included using the integrated mass flow controllers. Starting glucose concentration was 25 g/L. A constant concentrated sterile filtered glucose feed (500 g/L) was added to the tanks at specified rate, i.e. 2 g/h, once agitation reached 800 rpm. In cases where feed rate or dissolved oxygen content needed to be varied for robustness study, changes were made after cells entered stationary phase. Fermentation runs were extended for up to ˜50 hours after entry into stationary phase and samples automatically withdrawn every 3 hours. Samples were saved for subsequent analytical measurement.

In the case of growth associated fermentation processes, tanks were filled with 800 mL of FGM10 medium with 40 mM phosphate, which was in great excess and ensured phosphate depletion doesn't happen for growth associated fermentation processes. Antibiotics were added as appropriate. Frozen seed vials were thawed on ice and 7.5 mL of seed culture was used to inoculate the tanks. After inoculation, tanks were controlled at 37° C. and pH 6.8 using 5 M ammonium hydroxide and 1 M hydrochloric acid as titrants. The following oxygen control scheme was used to maintain the desired dissolved oxygen set point. First gas flow rate was increased from a minimum of 0.3 L/min of air to 0.8 L/min of air, subsequently, if more aeration was needed, agitation was increased from a minimum of 300 rpm to a maximum of 1000 rpm. Finally, if more oxygen was required to achieve the set point, oxygen supplementation was included using the integrated mass flow controllers. Starting glucose concentration was 25 g/L. A constant concentrated sterile filtered glucose feed (500 g/L) was added to the tanks at specified rate, i.e. 2 g/h, once agitation reached 800 rpm. Feed rate and dissolved oxygen concentration was set to desired values in the beginning, and maintained throughout the fermentation process. Fermentation runs were continued for up to ˜50 hours and samples automatically withdrawn every 3 hours. Samples were saved for subsequent analytical analysis.

Analytical Method

Sample standard curves for all compounds quantified are shown in Supplemental Materials, Section 10.

Glucose and Ethanol Quantification:

A UPLC-RI method was developed for the simultaneous quantification of glucose and ethanol concentrations, using an Acquity H-Class UPLC integrated with a Waters 2414 Refractive Index (RI) detector (Waters Corp., Milford, Mass. USA). Chromatographic separation was performed using a Bio-Rad Fast Acid Analysis HPLC Column (100×7.8 mm, 9 μm particle size; CAT #: #1250100, Bio-Rad Laboratories, Inc., Hercules, Calif.) at 65° C. 5 mM sulfuric acid was used as the eluent. The isocratic elution was as follows: 0-0.1 min, flow rate increased from 0.4 mL/min to 0.42 mL/min, 0.1-12 min flow rate at 0.48 mL/min. Sample injection volume was 10 μL. UPLC method development was carried out using standard aqueous stock solutions of analytes. Peak integration and further analysis was performed using MassLynx v4.1 software. The linear range used for glucose was 1-10 g/L, for ethanol was 1-20 g/L. Samples were diluted as needed to be within the accurate linear range. Dilution was performed using ultrapure water.

Alanine Quantification:

A reverse phase UPLC-MS/MS method was developed for alanine. Chromatographic separation was performed using a Restek Ultra AQ C18 column (150 mm×2.1 i.d., 3 μm; CAT #: 9178362, Restek Corporation, Bellefonte, Pa.) at 70° C. The following eluents were used: solvent A: H₂O, 0.2% formic acid and 0.05% ammonium (v/v); solvent B: MeOH, 0.2% formic acid and 0.05% ammonium (v/v). The gradient elution was as follows: 0-0.1 min isocratic 5% B, flow rate increased from 0.65 mL/min to 0.75 mL/min; 0.1-0.3 min, linear from 5% to 95% B at 0.75 mL/min; 0.3-0.9 min isocratic 95% B at 0.75 mL/min; and 0.9-1.2 min linear from 95% to 5% B at 0.75 mL/min; 1.2-1.3 min isocratic 5% B at 0.75 ml/min. Sample injection volume was 5 μL. UPLC method development was carried out using standard aqueous stock solutions of analyte. Separations were performed using an Acquity H-Class UPLC integrated with a Xevo™ TQD Mass spectrometer (Waters Corp., Milford, Mass. USA). MS/MS parameters including MRM transitions were tuned for each analyte and are listed in Table 22. Alanine (2,3-13C2, 99%) was used as internal standard for alanine at a concentration of 5 mg/L. Peak integration and further analysis was performed using MassLynx v4.1 software. The linear range for alanine was 1-100 mg/L. Samples were diluted as needed to be within the accurate linear range. Dilution was performed using ultrapure water, and the final 10-fold dilution was performed using solvent A, with 5 mg/L of C13 alanine (2,3-13C2, 99%)

Mevalonic Acid Quantification:

A reverse phase UPLC-TUV method was developed for the simultaneous quantification of mevalonic acid and mevalonolactone. Chromatographic separation was performed using a Restek Ultra AQ C18 column (150 mm×2.1 i.d., 3 μm; CAT #: 9178362, Restek Corporation, Bellefonte, Pa.) at 30° C. 20 mM phosphoric acid was used as the eluent. The isocratic elution was as follows: 0-3 min isocratic at 1 mL/min. Sample injection volume was 10 μL. Absorbance was monitored at 210 nm. UPLC method development was carried out using standard aqueous stock solutions of analytes. Separations were performed using an Acquity H-Class UPLC (Waters Corp., Milford, Mass. USA). Peak integration and further analysis was performed using MassLynx v4.1 software. The linear range for mevalonic acid and mevalonolactone were 0.01-0.1 g/L. Samples were diluted as needed to be within the accurate linear range. Mevalonic acid diluted in 20 mM phosphoric acid would spontaneously convert to mevalonolactone⁸⁰, thus, quantification of both mevalonic acid and mevalonolactone was necessary for fermentation samples. Mevalonic acid and mevalonolactone standards were prepared fresh each time, and ran immediately on UPLC. Dilution was performed using ultrapure water, and the final 10-fold dilution was performed using 20 mM phosphoric acid.

Alanine Stereoisomer Quantification:

A reverse phase UPLC-TUV method was developed for the simultaneous quantification and differentiation of L-/D-alanine. Chromatographic separation was performed using a Chirex 3126 (D)-penicillamine column (150×4.6 mm, 5 μm; Phenomenex Inc., Torrance, Calif.) at 50° C. 2 mM Copper Sulfate was used as the eluent. The isocratic elution was as follows: 0-10 min at 0.75 mL/min. Sample injection volume was 10 μL. Absorbance was monitored at 254 nm. UPLC method development was carried out using standard aqueous stock solutions of analytes. Separations were performed using an Acquity H-Class UPLC (Waters Corp., Milford, Mass. USA). Peak integration and further analysis was performed using MassLynx v4.1 software. The linear range for L-ID-alanine was 0.1-1 g/L. Samples were diluted as needed to be within the accurate linear range. Dilution was performed using ultrapure water.

Supplemental Materials

TABLE 1 Combinatorial complexity of metabolic networks. Entire E. coli Gene Network Reduced Central Metabolism Network Combination # Number of Experiments 1 4500 −45 (Glycolysis, TCA, PPP and ETC genes only) 2 1.0 × 10⁶  990 3 1.5 × 10¹⁰ 14,190 4 1.7 × 10¹³ 148,995 5 1.5 × 10¹⁶ 1.2 × 10⁶

Section 1: Phosphate Prometers

Phosphate promoter sequences were obtained from the EcoCyc database⁸¹ for PhoB regulated promoters (https://ecocyc.org/, Table 2). We sought to evaluate not only the relative strength of promoters previously characterized to respond to phosphate depletion, but in addition the relative leakiness in phosphate rich conditions. To this aim we constructed a set of fluorescent reporter plasmids. We cloned the ultraviolet excitable GFPuv gene behind a set of 12 phosphate dependent promoters, in the pSMART-HC-Kan (Lucigen, WI) backbone. These reporter strains were evaluated in a 2-stage micro-fermentation protocol in an m2p-labs Biolector™. Results are illustrated in FIG. 7. The ugpB gene promoter was often chosen for high level tightly controlled expression when expression cassettes were chromosomally integrated or for the inducible expression of guide arrays.

Insulators⁸² were added to both 5′ and 3′ end of a subset of phosphate promoters (Table 3) to help with consistent performance in different sequence contexts. To reduce read-through transcription, a unique terminator was added to the 5′ end of each insulated promoter. Terminator sequences were from http://parts.igem.org/Terminators/Catalog. Insulated phosphate promoters were similarly characterized using GFPuv expression in a m2p-labs Biolector™ (FIG. 8).

TABLE 2 Phosphate inducible promoter sequences evaluated, the ribosomal binding site is underlined, and the start codon of the gene (GFPuv) is shown in green. Promoter SEQ Name Sequence ID NO ugpBp TCTTTCTGACACCTTACTATCTTACAAATGTAACAAAAAAGTTATTTTTCTGTAATTCGA 1 GCATGTCATGTTACCCCGCGAGCATAAAACGCGTGTGTAGGAGGATAATCTATG yibDp GTGCGTAATTGTGCTGATCTCTTATATAGCTGCTCTCATTATCTCTCTACCCTGAAGTGAC 2 TCTCTCACCTGTAAAAATAATATCTCACAGGCTTAATAGTTTCTTAATACAAAGCCTGTA AAACGTCAGGATAACTTCTGTGTAGGAGGATAATCTATG phoAp CGATTACGTAAAGAAGTTATTGAAGCATCCTCGTCAGTAAAAAGTTAATCTTTTCAACA 3 GCTGTCATAAAGTTGTCACGGCCGAGACTTATAGTCGCTTTGTTTTTATTTTTTAATGTAT TTGTAGTGTAGGAGGATAATCTATGGCTAGCAAAGGAGAAGAACTTTTCACATG phoBp GCCACGGAAATCAATAACCTGAAGATATGTGCGACGAGCTTTTCATAAATCTGTCATAA 4 ATCTGACGCATAATGACGTCGCATTAATGATCGCAACCTATTTATTGTGTAGGAGGATA ATCTATGGCTAGCAAAGGAGAAGAACTTTTCACATG amnp AGACAGTCAACGCGCTTGATAGCCTGGCGAAGATCATCCGATCTTCGCCTTACACTTTTG 5 TTTCACATTTCTGTGACATACTATCGGATGTGCGGTAATTGTATAGGAGGATAATCTATG ydfHp GCTATGCCGGACTGAATGTCCACCGTCAGTAATTTTTATACCCGGCGTAACTGCCGGGTT 6 ATTGCTTGTCACAAAAAAGTGGTAGACTCATGCAGTTAACTCACTGTGTAGGAGGATAA TCTATG mipAp CATCCATAAATTTTGCATAATTAATGTAAAGACCAGGCTCGCCAGTAACGCTAAATTCA 7 TTTGGCTGTAAGCGCGGTGTCATCCGCGTCAGGAAAATTAAACAGTTACTTTAAAAAAT GAAAACGTAAAAAGGTTGGGTTTCGATGTATTGACGGGTAAACTTTGTCGCCCGCTAAA CATTTGTTTGTGTAGGAGGATAATCTATG phoHp AATCCTGCTGAAAGCACACAGCTTTTTTCATCACTGTCATCACTCTGTCATCTTTCCAGT 8 AGAAACTAATGTCACTGAAATGGTGTTTTATAGTTAAATATAAGTAAATATATTGTTGCA ATAAATGCGAGATCTGTTGTACTTATTAAGTAGCAGCGGAAGTTCGTGTAGGAGGATAA TCTAT yhjCp CTACAGAGATGACGTGTAGAAAATAGTTACCGATATAAATAGTTACAGCTAAACGCCTG 9 AAATTACATGTCGAGGGCACTATTTAAAACAATTTTGAGGATTTCCTTATATTGGTGGTT AGTACGCATGCAATTAAAAATGAAATTCCGCGACCACAAGCCAAAATAACAAACGGCA AGGAGACAAAAATAAGCACAAATAGCCAACACGTCCTCTGTTCACTTTAAAGGGAATCG CTGAAAAATACGCTCTGTTTAAGGGGATTCACCTTTCTCAGAAAGCTATTCCGCCCTTTT CCTGCTGAGAAATCGCCACATTCGGCATGACAACATTGTGAAAGTGTAGGAGGATAATC TATG phoUp ACCGAACTGAAGCAGGATTACACCGTGGTGATCGTCACCCACAACATGCAGCAGGCTGC 10 GCGTTGTTCCGACCACACGGCGTTTATGTACCTGGGCGAATTGATTGAGTTCAGCAACA CGGACGATCTGTTCACCAGTGTAGGAGGATAATCTATG pstSp AAGACTTTATCTCTCTGTCATAAAACTGTCATATTCCTTACATATAACTGTCACCTGTTTG 11 TCCTATTTTGCTTCTCGTAGCCAACAAACAATGCTTTATGAGTGTAGGAGGATAATCTAT GGCTAGCAAAGGAGAAGAACTTTTCACATG phoEp AGCATGGCGTTTTGTTGCGCGGGATCAGCAAGCCTAGCGGCAGTTGTTTACGCTTTTATT 12 ACAGATTTAATAAATTACCACATTTTAAGAATATTATTAATCTGTAATATATCTTTAACA ATCTCAGGTTAAAAACTTTCCTGTTTTCAACGGGACTCTCCCGCTGGTGTAGGAGGATAA TCTATG

TABLE 3 Insulated promoter sequences. Insulator sequences are italicized. −35 and −10 boxes are highlighted in bold and underlined. SEQ Insulated Promoter Sequence ID NO BBa_B0015_IN_yibDp CCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTT 13 TTATCTGTTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCT TCGGGTGGGCCTTTCTGCGTTTATACACAGCTAACACCACGTCGTCCCTATCTG CTGCCCTAGGTCTATGAGTGGTTGCTGGATAACGTGCGTAATTGTGCTGATCTC TTATATAGCTGCTCTCATTATCTCTCTACCCTGAA GTGACT CTCTCACCTGTA AAAATAATATCTCACAGGCT TAATA GTTTCTTAATACAAAGCCTGTAAAACG TCAGGATAACTTCTATATTCAGGGAGACCACAACGGTTTCCCTCTACAAATAATTT TGTTTAACTTT BBa_B1002_IN_phoBp CGCAAAAAACCCCGCTTCGGCGGGGTTTTTTCGCACGTCTCCATCGCTTGCC 14 CAAGTTGTGAAGCACAGCTAACACCACGTCGTCCCTATCTGCTGCCCTAGGTCT ATGAGTGGTTGCTGGATAACGCCACGGAAATCAATAACCTGAAGATATGTGCG ACGAGCTT TTCATA AATCTGTCATAAATCTGACG CATAAT GACGTCGCATTA ATGATCGCAACCTATTTATTATATTCAGGGAGACCACAACGGTTTCCCTCTACAA ATAATTTTGTTTAACTTT BBa_B1004_IN_mipAp CGCCGAAAACCCCGCTTCGGCGGGGTTTTGCCGCACGTCTCCATCGCTTGCC 15 CAAGTTGTGAAGCACAGCTAACACCACGTCGTCCCTATCTGCTGCCCTAGGTCT ATGAGTGGTTGCTGGATAACCATCCATAAATTTTGCATAATTAATGTAAAGAC CAGGCTCGCCAGTAACGCTAAATTCATTTGGCTGTAAGCGCGGTGTCATCCG CGTCAGGAAAATTAAACAGTTACTTTAAAAAATGAAAACGTAAA AAGGTT G GGTTTCGATGTATTGACGG GTAAAC TTTGTCGCCCGCTAAACATTTGTTTATA TTCAGGGAGACCACAACGGTTTCCCTCTACAAATAATTTTGTTTAACTTT BBa_B1006_IN_phoUp AAAAAAAAACCCCGCCCCTGACAGGGCGGGGTTTTTTTTACGTCTCCATCGC 16 TTGCCCAAGTTGTGAAGCACAGCTAACACCACGTCGTCCCTATCTGCTGCCCTA GGTCTATGAGTGGTTGCTGGATAACACCGAACTGAAGCAGGATTACACCGTGG TGATCGTCACCCACAACATGCAGCAGGCTGCGCGTTGTTCCGACCACA CGG CGT TTATGTACCTGGGCGAATT GATTGA GTTCAGCAACACGGACGATCTGTT CACCAATATTCAGGGAGACCACAACGGTTTCCCTCTACAAATAATTTTGTTTAACTT T BBa_B1010_IN_phoHp CGCCGCAAACCCCGCCCCTGACAGGGCGGGGTTTCGCCGCACGTCTCCATCG 17 CTTGCCCAAGTTGTGAAGCACAGCTAACACCACGTCGTCCCTATCTGCTGCCCT AGGTCTATGAGTGGTTGCTGGATAACAATCCTGCTGAAAGCACACAGCTTTTTT CATCACTGTCATCACT CTGTCA TCTTTCCAGTAGAAAC TAATGT CACTGAAA TGGTGTTTTATAGTTAAATATAAGTAAATATATTGTTGCAATAAATGCGAGA TCTGTTGTACTTATTAAGTAGCAGCGGAAGTTCATATTCAGGGAGACCACAAC GGTTTCCCTCTACAAATAATTTTGTTTAACTTT

Section, 2: Constitutive Promoters

A set of constitutive insulated promoters of varying strength were used for constitutive expression and taken directly from Davis et al., including the proA, proB, proC, proD promoters⁸² and HCEp promoter⁸³. Insulator was added to 5′ and 3′ of HCEp promoter. Similar to insulated phosphate promoters, a unique terminator was added to the 5′ end of constitutive promoters. These were used to drive constitutive pathway expression in growth associated production strains as well as to make strain modifications where constitutive heterologous gene expression was appropriate. These promoter sequences are given in Table 4 below and promoter characterized using GFPuv expression (FIG. 9).

TABLE 4 Constitutive promoter sequences. Promoter Sequence SEQ ID NO BBa_B1004_proA CGCCGAAAACCCCGCTTCGGCGGGGTTTTGCCGCACGTC 18 TCCATCGCTTGCCCAAGTTGTGAAGCACAGCTAACACCA CGTCGTCCCTATCTGCTGCCCTAGGTCTATGAGTGGTTG CTGGATAACTTTACGGGCATGCATAAGGCTCGTAGGCTA TATTCAGGGAGACCACAACGGTTTCCCTCTACAAATAAT TTTGTTTAACTTT BBa_B1006_proB AAAAAAAAACCCCGCCCCTGACAGGGCGGGGTTTTTTTT 19 ACGTCTCCATCGCTTGCCCAAGTTGTGAAGCACAGCTAA CACCACGTCGTCCCTATCTGCTGCCCTAGGTCTATGAGT GGTTGCTGGATAACTTTACGGGCATGCATAAGGCTCGTA ATATATATTCAGGGAGACCACAACGGTTTCCCTCTACAA ATAATTTTGTTTAACTTT BBa_B1010_proC CGCCGCAAACCCCGCCCCTGACAGGGCGGGGTTTCGCC 20 GCACGTCTCCATCGCTTGCCCAAGTTGTGAAGCACAGCT AACACCACGTCGTCCCTATCTGCTGCCCTAGGTCTATGA GTGGTTGCTGGATAACTTTACGGGCATGCATAAGGCTCG TATGATATATTCAGGGAGACCACAACGGTTTCCCTCTAC AAATAATTTTGTTTAACTTT BBa_B1002_proD CGCAAAAAACCCCGCTTCGGCGGGGTTTTTTCGCACGTC 21 TCCATCGCTTGCCCAAGTTGTGAAGCACAGCTAACACCA CGTCGTCCCTATCTGCTGCCCTAGGTCTATGAGTGGTTG CTGGATAACTTTACGGGCATGCATAAGGCTCGTATAATA TATTCAGGGAGACCACAACGGTTTCCCTCTACAAATAAT TTTGTTTAACTTT BBa_B0015_IN_HCEp CCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGAC 22 TGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTC TCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTT TCTGCGTTTATACACAGCTAACACCACGTCGTCCCTATC TGCTGCCCTAGGTCTATGAGTGGTTGCTGGATAACCTCC TTCACAGATTCCCAATCTCTTGTTAAATAACGAAAAAGC ATCAATTAAAACCCATGTCTTTCTATATTCCAGCAATGT TTTATAGGGGACATATTGATGAAGATGGGTATCACCTTA GTGAATTGCTATAAGCTGCTCTTTTTTGTTCGTGATATAC TGATAAATTGAATTTTCACACTTCATATTCAGGGAGACC ACAACGGTTTCCCTCTACAAATAATTTTGTTTAACTTT

Section 3: Chromosomally Modified Host Strains

FIG. 11 depicts each chromosomal modification. Strains utilized and/or constructed for this study are listed in Table 5. Tables 6 and 7 lists oligonucleotides and synthetic DNA sequences used for strain construction and/or confirmation. FIG. 12 and FIG. 13A-E show growth rates and glucose distribution during growth for control strains in 1 L fermentation.

TABLE 5 List of chromosomally modified strains. Strain Genotype Source BW25113 (wt) F-, λ-, Δ(araD-araB)567, lacZ4787(del)(::rrnB-3), rph-1, CGSC Δ(rhaD-rhaB)568, hsdR514 JW3197-1 BW25113, sspB756(del)::kan ⁵³ Bwapldf BW25113, ΔackA-pta, ΔpoxB, ΔpflB, ΔldhA, ΔadhE ³⁹ DLF_0001 BWapldf, ΔiclR, ΔarcA this study DLF_0002 BWapldf, ΔiclR, ΔarcA, ΔsspB::frt this study DLF_0025 DLF_0002, Δcas3::tm-ugpb-sspB-pro-casA(N2S) this study DLF_0028 DLF_0025, fabI-DAS + 4-gentR this study DLF_0031 DLF_0025, lpd-DAS + 4-gentR this study DLF_0038 DLF_0025, fabI-DAS + 4-gentR, udhA-DAS + 4-bsdR this study DLF_0039 DLF_0025, fabI-DAS + 4-gentR, gltA-DAS + 4-zeoR this study DLF_0040 DLF_0025, fabI-DAS + 4-gentR, zwf-DAS + 4-bsdR this study DLF_0041 DLF_0025, lpd-DAS + 4-gentR, gltA-DAS + 4-zeoR this study DLF_0042 DLF_0025, lpd-DAS + 4-gentR, udhA-DAS + 4-bsdR this study DLF_0043 DLF_0025, gltA-DAS + 4-zeoR this study DLF_0044 DLF_0025, gltA-DAS + 4-zeoR, zwf-DAS + 4-bsdR this study DLF_0045 DLF_0025, gltA-DAS + 4-zeoR, udhA-DAS + 4-bsdR this study DLF_0046 DLF_0025, fabI-DAS + 4-gentR, gltA-DAS + 4-zeoR, zwf-DAS + 4-bsdR this study DLF_0047 DLF_0025, fabI-DAS + 4-gentR, gltA-DAS + 4::zeoR, udhA-DAS + 4-bsdR this study DLF_0048 DLF_0025, lpd-DAS + 4-gentR, gltA-DAS + 4-zeoR, zwf-DAS + 4-bsdR this study DLF_0049 DLF_0025, lpd-DAS + 4-gentR, gltA-DAS + 4-zeoR, udhA-DAS + 4-bsdR this study DLF_0165 DLF_0025, lpd-DAS + 4-gentR, zwf-DAS + 4-bsdR this study DLF_0763 DLF_0025, udhA-DAS + 4-bsdR this study DLF_01002 DLF_0025, zwf-DAS + 4-bsdR this study DLF_01517 DLF_0012, Δcas3::pro-casA(N2S) this study DLF_01530 DLF_0025, fabI-DAS + 4-gentR, udhA-DAS + 4-bsdR, zeoR-proDp-gapN-zeoR this study DLF_01531 DLF_0025, fabI-DAS + 4-gentR, udhA-DAS + 4-bsdR, gltA-DAS + 4-purR this study DLF_01532 DLF_0025, fabI-DAS + 4-gentR, udhA-DAS + 4-bsdR, gapA-DAS + this study 4-zeoR-proDp-gapN DLF_01533 DLF_0025, fabI-DAS + 4-gentR, udhA-DAS + 4-bsdR, gapA-DAS + this study 4-zeoR-proDp-gapN, gltA-DAS + 4-purR DLF_01536 DLF_0025, fabI-DAS + 4-gentR, udhA-DAS + 4-bsdR, zeoR-proDp-gapN, this study gltA-DAS + 4-purR DLF_01537 DLF_0025, fabI-DAS + 4-gentR, udhA-DAS + 4-bsdR, gapA-DAS + 4-zeoR this study DLF_01538 DLF_0025, fabI-DAS + 4-gentR, gltA-DAS + 4-zeoR, udhA-DAS+ 4-bsdR, this study gapA-DAS + 4-zeoR

TABLE 6 Oligonucleotides utilized for strain construction. Oligo Sequence SEQ ID NO ilcR_tetA_F TAACAATAAAAATGAAAATGATTTCCACGATACAGAAA 23 AAAGAGACTGTCATCCTAATTTTTGTTGACACTCTATC ilcR_sacB_R TGCCACTCAGGTATGATGGGCAGAATATTGCCTCTGCCC 24 GCCAGAAAAAGATCAAAGGGAAAACTGTCCATATGC iclR_500up CCGACAGGGATTCCATCTG 25 iclR_500dn TATGACGACCATTTTGTCTACAGTTC 26 arcA_tetA_F GGACTTTTGTACTTCCTGTTTCGATTTAGTTGGCAATTTA 27 GGTAGCAAACTCCTAATTTTTGTTGACACTCTATC arcA_sacB_R ATAAAAACGGCGCTAAAAAGCGCCGTTTTTTTTGACGGT 28 GGTAAAGCCGAATCAAAGGGAAAACTGTCCATATGC arcA_500up CCTGACTGTACTAACGGTTGAG 29 arcA_500dn TGACTTTTATGGCGTTCTTTGTTTTTG 30 sspB_kan_F CTGGTACACGCTGATGAACACC 31 sspB_kan_R CTGGTCATTGCCATTTGTGCC 32 sspB_conf_F GAATCAGAGCGTTCCGACCC 33 sspB_conf_R GTACGCAGTTTGCCAACGTG 34 cas3_tetA_F AATAGCCCGCTGATATCATCGATAATACTAAAAAAACAG 35 GGAGGCTATTATCCTAATTTTTGTTGACACTCTATC cas3_sacB_R TACAGGGATCCAGTTATCAATAAGCAAATTCATTTGTTCT 36 CCTTCATATGATCAAAGGGAAAACTGTCCATATGC cas3_conf_F CAAGACATGTGTATATCACTGTAATTC 37 cas3_500dn GCGATTGCAGATTTATGATTTGG 38 fabl_conf_F GCAAAATGCTGGCTCATTG 39 gapA_conf_F GAACTGAATGGCAAACTGACTG 40 gapA_500dn TGGGGATGATCGACCACA 41 gltA_conf_F TATCATCCTGAAAGCGATGG 42 lpd_conf_F ATCTCACCGTGTGATCGG 43 udhA_conf_F CAAAAGAGATTCTGGGTATTCACT 44 zwf_conf_F CTGCTGGAAACCATGCG 45 zwf_500dn AGAGCATGTCGTTATAGGAGGTGAT 46 ampR_intR AGTACTCAACCAAGTCATTCTG 47 bsdR_intR GAGCATGGTGATCTTCTCAGT 48 gntR_intR GCGATGAATGTCTTACTACGGA 49 purR_intR GTCGCTGGGTAATCTGCAA 50 tetA_intR ATCAACGCATATAGCGCTAGCAG 51 zeoR_intR ACTGAAGCCCAGACGATC 52

TABLE 7 Synthetic DNA utilized for strain construction. SEQ ID NO tetA-sacB Cassette 53 TCCTAATTTTTGTTGACACTCTATCATTGATAGAGTTATTTTACCACTCCCTA TCAGTGATAGAGAAAAGTGAAATGAATAGTTCGACAAAGATCGCATTGGTA ATTACGTTACTCGATGCCATGGGGATTGGCCTTATCATGCCAGTCTTGCCAA CGTTATTACGTGAATTTATTGCTTCGGAAGATATCGCTAACCACTTTGGCGT ATTGCTTGCACTTTATGCGTTAATGCAGGTTATCTTTGCTCCTTGGCTTGGAA AAATGTCTGACCGATTTGGTCGGCGCCCAGTGCTGTTGTTGTCATTAATAGG CGCATCGCTGGATTACTTATTGCTGGCTTTTTCAAGTGCGCTTTGGATGCTGT ATTTAGGCCGTTTGCTTTCAGGGATCACAGGAGCTACTGGGGCTGTCGCGGC ATCGGTCATTGCCGATACCACCTCAGCTTCTCAACGCGTGAAGTGGTTCGGT TGGTTAGGGGCAAGTTTTGGGCTTGGTTTAATAGCGGGGCCTATTATTGGTG GTTTTGCAGGAGAGATTTCACCGCATAGTCCCTTTTTTATCGCTGCGTTGCTA AATATTGTCACTTTCCTTGTGGTTATGTTTTGGTTCCGTGAAACCAAAAATAC ACGTGATAATACAGATACCGAAGTAGGGGTTGAGACGCAATCGAATTCGGT ATACATCACTTTATTTAAAACGATGCCCATTTTGTTGATTATTTATTTTTCAG CGCAATTGATAGGCCAAATTCCCGCAACGGTGTGGGTGCTATTTACCGAAA ATCGTTTTGGATGGAATAGCATGATGGTTGGCTTTTCATTAGCGGGTCTTGG TCTTTTACACTCAGTATTCCAAGCCTTTGTGGCAGGAAGAATAGCCACTAAA TGGGGCGAAAAAACGGCAGTACTGCTCGGATTTATTGCAGATAGTAGTGCA TTTGCCTTTTTAGCGTTTATATCTGAAGGTTGGTTAGTTTTCCCTGTTTTAATT TTATTGGCTGGTGGTGGGATCGCTTTACCTGCATTACAGGGAGTGATGTCTA TCCAAACAAAGAGTCATCAGCAAGGTGCTTTACAGGGATTATTGGTGAGCC TTACCAATGCAACCGGTGTTATTGGCCCATTACTGTTTGCTGTTATTTATAAT CATTCACTACCAATTTGGGATGGCTGGATTTGGATTATTGGTTTAGCGTTTTA CTGTATTATTATCCTGCTATCGATGACCTTCATGTTAACCCCTCAAGCTCAGG GGAGTAAACAGGAGACAAGTGCTTAGTTATTTCGTCACCAAATGATGTTATT CCGCGAAATATAATGACCCTCTTGATAACCCAAGAGCATCACATATACCTGC CGTTCACTATTATTTAGTGAAATGAGATATTATGATATTTTCTGAATTGTGAT TAAAAAGGCAACTTTATGCCCATGCAACAGAAACTATAAAAAATACAGAGA ATGAAAAGAAACAGATAGATTTTTTAGTTCTTTAGGCCCGTAGTCTGCAAAT CCTTTTATGATTTTCTATCAAACAAAAGAGGAAAATAGACCAGTTGCAATCC AAACGAGAGTCTAATAGAATGAGGTCGAAAAGTAAATCGCGCGGGTTTGTT ACTGATAAAGCAGGCAAGACCTAAAATGTGTAAAGGGCAAAGTGTATACTT TGGCGTCACCCCTTACATATTTTAGGTCTTTTTTTATTGTGCGTAACTAACTT GCCATCTTCAAACAGGAGGGCTGGAAGAAGCAGACCGCTAACACAGTACAT AAAAAAGGAGACATGAACGATGAACATCAAAAAGTTTGCAAAACAAGCAA CAGTATTAACCTTTACTACCGCACTGCTGGCAGGAGGCGCAACTCAAGCGTT TGCGAAAGAAACGAACCAAAAGCCATATAAGGAAACATACGGCATTTCCCA TATTACACGCCATGATATGCTGCAAATCCCTGAACAGCAAAAAAATGAAAA ATATCAAGTTCCTGAGTTCGATTCGTCCACAATTAAAAATATCTCTTCTGCA AAAGGCCTGGACGTTTGGGACAGCTGGCCATTACAAAACGCTGACGGCACT GTCGCAAACTATCACGGCTACCACATCGTCTTTGCATTAGCCGGAGATCCTA AAAATGCGGATGACACATCGATTTACATGTTCTATCAAAAAGTCGGCGAAA CTTCTATTGACAGCTGGAAAAACGCTGGCCGCGTCTTTAAAGACAGCGACA AATTCGATGCAAATGATTCTATCCTAAAAGACCAAACACAAGAATGGTCAG GTTCAGCCACATTTACATCTGACGGAAAAATCCGTTTATTCTACACTGATTT CTCCGGTAAACATTACGGCAAACAAACACTGACAACTGCACAAGTTAACGT ATCAGCATCAGACAGCTCTTTGAACATCAACGGTGTAGAGGATTATAAATC AATCTTTGACGGTGACGGAAAAACGTATCAAAATGTACAGCAGTTCATCGA TGAAGGCAACTACAGCTCAGGCGACAACCATACGCTGAGAGATCCTCACTA CGTAGAAGATAAAGGCCACAAATACTTAGTATTTGAAGCAAACACTGGAAC TGAAGATGGCTACCAAGGCGAAGAATCTTTATTTAACAAAGCATACTATGG CAAAAGCACATCATTCTTCCGTCAAGAAAGTCAAAAACTTCTGCAAAGCGA TAAAAAACGCACGGCTGAGTTAGCAAACGGCGCTCTCGGTATGATTGAGCT AAACGATGATTACACACTGAAAAAAGTGATGAAACCGCTGATTGCATCTAA CACAGTAACAGATGAAATTGAACGCGCGAACGTCTTTAAAATGAACGGCAA ATGGTACCTGTTCACTGACTCCCGCGGATCAAAAATGACGATTGACGGCATT ACGTCTAACGATATTTACATGCTTGGTTATGTTTCTAATTCTTTAACTGGCCC ATACAAGCCGCTGAACAAAACTGGCCTTGTGTTAAAAATGGATCTTGATCCT AACGATGTAACCTTTACTTACTCACACTTCGCTGTACCTCAAGCGAAAGGAA ACAATGTCGTGATTACAAGCTATATGACAAACAGAGGATTCTACGCAGACA AACAATCAACGTTTGCGCCAAGCTTCCTGCTGAACATCAAAGGCAAGAAAA CATCTGTTGTCAAAGACAGCATCCTTGAACAAGGACAATTAACAGTTAACA AATAAAAACGCAAAAGAAAATGCCGATATTGACTACCGGAAGCAGTGTGAC CGTGTGCTTCTCAAATGCCTGATTCAGGCTGTCTATGTGTGACTGTTGAGCT GTAACAAGTTGTCTCAGGTGTTCAATTTCATGTTCTAGTTGCTTTGTTTTACT GGTTTCACCTGTTCTATTAGGTGTTACATGCTGTTCATCTGTTACATTGTCGA TCTGTTCATGGTGAACAGCTTTAAATGCACCAAAAACTCGTAAAAGCTCTGA TGTATCTATCTTTTTTACACCGTTTTCATCTGTGCATATGGACAGTTTTCCCTT TGAT ΔiclR-cure 54 AAATGATTTCCACGATACAGAAAAAAGAGACTGTCATGGGCAGAATATTGC CTCTGCCCGCCAGAAAAAG ΔarcA-cure 55 CTGTTTCGATTTAGTTGGCAATTTAGGTAGCAAACTCGGCTTTACCACCGTC AAAAAAAACGGCGCTTTT Δcas3-pro-casA 56 CAAGACATGTGTATATCACTGTAATTCGATATTTATGAGCAGCATCGAAAAA TAGCCCGCTGATATCATCGATAATACTAAAAAAACAGGGAGGCTATTACCA GGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTA TCTGTTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCG GGTGGGCCTTTCTGCGTTTATATCTTTCTGACACCTTACTATCTTACAAATGT AACAAAAAAGTTATTTTTCTGTAATTCGAGCATGTCATGTTACCCCGCGAGC ATAAAACGCGTGTGTAGGAGGATAATCTTTGACGGCTAGCTCAGTCCTAGGT ACAGTGCTAGCCATATGAAGGAGAACAAATGAATTTGCTTATTGATAACTG GATCCCTGTACGCCCGCGAAACGGGGGGAAAGTCCAAATCATAAATCTGCA ATCGCTATAC Δcas3::ugBp-sspB-pro-casA 57 CAAGACATGTGTATATCACTGTAATTCGATATTTATGAGCAGCATCGAAAAA TAGCCCGCTGATATCATCGATAATACTAAAAAAACAGGGAGGCTATTACCA GGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTA TCTGTTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCG GGTGGGCCTTTCTGCGTTTATATCTTTCTGACACCTTACTATCTTACAAATGT AACAAAAAAGTTATTTTTCTGTAATTCGAGCATGTCATGTTACCCCGCGAGC ATAAAACGCGTGTGTAGGAGGATAATCTATGGATTTGTCACAGCTAACACC ACGTCGTCCCTATCTGCTGCGTGCATTCTATGAGTGGTTGCTGGATAACCAG CTCACGCCGCACCTGGTGGTGGATGTGACGCTCCCTGGCGTGCAGGTTCCTA TGGAATATGCGCGTGACGGGCAAATCGTACTCAACATTGCGCCGCGTGCTGT CGGCAATCTGGAACTGGCGAATGATGAGGTGCGCTTTAACGCGCGCTTTGGT GGCATTCCGCGTCAGGTTTCTGTGCCGCTGGCTGCCGTGCTGGCTATCTACG CCCGTGAAAATGGCGCAGGCACGATGTTTGAGCCTGAAGCTGCCTACGATG AAGATACCAGCATCATGAATGATGAAGAGGCATCGGCAGACAACGAAACC GTTATGTCGGTTATTGATGGCGACAAGCCAGATCACGATGATGACACTCATC CTGACGATGAACCTCCGCAGCCACCACGCGGTGGTCGACCGGCATTACGCG TTGTGAAGTAATTGACGGCTAGCTCAGTCCTAGGTACAGTGCTAGCCATATG AAGGAGAACAAATGAATTTGCTTATTGATAACTGGATCCCTGTACGCCCGCG AAACGGGGGGAAAGTCCAAATCATAAATCTGCAATCGCTATAC facI-DAS + 4-gentR 58 CTATTGAAGATGTGGGTAACTCTGCGGCATTCCTGTGCTCCGATCTCTCTGC CGGTATCTCCGGTGAAGTGGTCCACGTTGACGGCGGTTTCAGCATTGCTGCA ATGAACGAACTCGAACTGAAAGCGGCCAACGATGAAAACTATTCTGAAAAC TATGCGGATGCGTCTTAATAGGAAGTTCCTATTCTCTAGAAAGTATAGGAAC TTCCGAATCCATGTGGGAGTTTATTCTTGACACAGATATTTATGATATAATA ACTGAGTAAGCTTAACATAAGGAGGAAAAACATATGTTACGCAGCAGCAAC GATGTTACGCAGCAGGGCAGTCGCCCTAAAACAAAGTTAGGTGGCTCAAGT ATGGGCATCATTCGCACATGTAGGCTCGGCCCTGACCAAGTCAAATCCATGC GGGCTGCTCTTGATCTTTTCGGTCGTGAGTTCGGAGACGTAGCCACCTACTC CCAACATCAGCCGGACTCCGATTACCTCGGGAACTTGCTCCGTAGTAAGACA TTCATCGCGCTTGCTGCCTTCGACCAAGAAGCGGTTGTTGGCGCTCTCGCGG CTTACGTTCTGCCCAAGTTTGAGCAGCCGCGTAGTGAGATCTATATCTATGA TCTCGCAGTCTCCGGCGAGCACCGGAGGCAGGGCATTGCCACCGCGCTCAT CAATCTCCTCAAGCATGAGGCCAACGCGCTTGGTGCTTATGTGATCTACGTG CAAGCAGATTACGGTGACGATCCCGCAGTGGCTCTCTATACAAAGTTGGGC ATACGGGAAGAAGTGATGCACTTTGATATCGACCCAAGTACCGCCACCTAA GAAGTTCCTATTCTCTAGAAAGTATAGGAACTTCCGTTCTGTTGGTAAAGAT GGGCGGCGTTCTGCCGCCCGTTATCTCTGTTATACCTTTCTGATATTTGTTAT CGCCGATCCGTCTTTCTCCCCTTCCCGCCTTGCGTCAGG gapA-DAS + 4-zeoR-proDp-gapN 59 TCTCCAAAGCGGCCAACGATGAAAACTATTCTGAAAACTATGCGGATGCGT CTTGATTGACAGCTAGCTCAGTCCTAGGTATAATGCTAGCAACTTTAAAATT AAAGAGGTATATATTAATGACTAAGCAATATAAGAATTACGTAAATGGGGA GTGGAAGCTTTCGGAGAATGAAATTAAGATCTATGAACCAGCCAGTGGGGC GGAATTGGGGTCAGTCCCGGCAATGTCCACTGAAGAAGTTGACTATGTCTAC GCCTCGGCCAAAAAAGCGCAGCCAGCATGGCGCTCGCTTTCCTATATTGAGC GTGCGGCTTATTTGCACAAAGTCGCAGACATCCTGATGCGTGACAAGGAGA AAATTGGAGCGGTATTGTCCAAGGAAGTAGCGAAAGGCTACAAATCCGCAG TATCGGAGGTCGTCCGCACCGCCGAGATTATTAATTATGCGGCCGAAGAAG GGCTTCGCATGGAGGGTGAGGTCTTGGAGGGCGGCAGTTTTGAGGCGGCAT CCAAGAAAAAAATCGCTGTCGTCCGTCGCGAGCCGGTGGGACTTGTGCTTG CTATTAGTCCGTTCAATTACCCCGTGAATCTGGCCGGCTCCAGATTGCCCC TGCACTGATCGCGGGCAATGTAATCGCTTTTAAACCACCGACCCAAGGATCG ATTAGTGGACTTCTTTTAGCGGAGGCGTTTGCGGAGGCAGGTCTTCCAGCCG GCGTATTCAATACCATCACGGGGCGTGGAAGTGAAATCGGGGATTACATCG TGGAGCACCAGGCAGTAAATTTCATCAACTTCACGGGTTCCACGGGGATCG GGGAGCGTATCGGTAAGATGGCTGGGATGCGTCCGATCATGTTGGAACTTG GCGGCAAGGATAGTGCGATTGTGCTGGAAGACGCAGACTTGGAATTGACAG CTAAAAACATTATCGCTGGAGCCTTCGGGTATAGTGGTCAACGTTGCACGGC AGTTAAGCGCGTTCTTGTTATGGAAAGTGTCGCGGATGAATTGGTCGAGAA GATTCGCGAGAAAGTGTTAGCTCTTACGATTGGAAATCCAGAGGACGATGC TGACATCACTCCATTGATCGACACGAAATCCGCGGATTACGTCGAGGGGCT GATCAACGACGCGAACGATAAGGGAGCAGCGGCTTTGACCGAGATCAAACG CGAGGGGAACCTGATCTGCCCGATTCTTTTTGACAAAGTCACAACTGACATG CGCTTGGCATGGGAAGAACCCTTCGGCCCAGTCTTGCCTATTATCCGCGTTA CTAGCGTAGAGGAAGCAATTGAAATTTCCAATAAATCCGAATATGGGTTGC AAGCGAGTATCTTTACTAACGATTTTCCACGTGCCTTTGGTATTGCGGAACA GTTAGAAGTCGGGACAGTTCACATCAACAACAAGACGCAGCGCGGGACAGA TAACTTCCCCTTTTTGGGAGCAAAGAAGTCTGGGGCTGGAATCCAAGGGGT GAAATACTCCATCGAAGCCATGACGACGGTGAAGAGCGTTGTTTTTGACATC AAGTAAAACATAAGGAGGAAAAACAGATGGCGAAACTGACCTCGGCGGTT CCGGTTCTGACGGCACGTGATGTGGCGGGCGCGGTTGAATTTTGGACGGATC GTCTGGGCTTCAGTCGTGATTTTGTGGAAGATGACTTCGCAGGCGTGGTTCG CGATGACGTCACCCTGTTTATTTCCGCAGTTCAGGATCAAGTCGTGCCGGAC AACACGCTGGCTTGGGTGTGGGTTCGTGGCCTGGATGAACTGTATGCGGAAT GGAGCGAAGTTGTCTCTACCAATTTCCGTGACGCGAGCGGTCCGGCCATGAC GGAAATCGGCGAACAGCCGTGGGGTCGCGAATTTGCTCTGCGTGACCCGGC TGGCAACTGTGTCCATTTCGTGGCTGAAGAACAAGATTGAGTTGAGATGAC ACTGTGATCTAAAAAGAGCGACTTCGGTCGCTCTTTTTTTTACCTGA gapA-zeoR-proDp-gapN 60 ACGAAACCGGTTACTCCAACAAAGTTCTGGACCTGATCGCTCACATCTCCAA ATGATTGACAGCTAGCTCAGTCCTAGGTATAATGCTAGCAACTTTAAAATTA AAGAGGTATATATTAATGACTAAGCAATATAAGAATTACGTAAATGGGGAG TGGAAGCTTTCGGAGAATGAAATTAAGATCTATGAACCAGCCAGTGGGGCG GAATTGGGGTCAGTCCCGGCAATGTCCACTGAAGAAGTTGACTATGTCTACG CCTCGGCCAAAAAAGCGCAGCCAGCATGGCGCTCGCTTTCCTATATTGAGCG TGCGGCTTATTTGCACAAAGTCGCAGACATCCTGATGCGTGACAAGGAGAA AATTGGAGCGGTATTGTCCAAGGAAGTAGCGAAAGGCTACAAATCCGCAGT ATCGGAGGTCGTCCGCACCGCCGAGATTATTAATTATGCGGCCGAAGAAGG GCTTCGCATGGAGGGTGAGGTCTTGGAGGGCGGCAGTTTTGAGGCGGCATC CAAGAAAAAAATCGCTGTCGTCCGTCGCGAGCCGGTGGGACTTGTGCTTGCT ATTAGTCCGTTCAATTACCCCGTGAATCTGGCCGGCTCCAAGATTGCCCCTG CACTGATCGCGGGCAATGTAATCGCTTTTAAACCACCGACCCAAGGATCGAT TAGTGGACTTCTTTTAGCGGAGGCGTTTGCGGAGGCAGGTCTTCCAGCCGGC GTATTCAATACCATCACGGGGCGTGGAAGTGAAATCGGGGATTACATCGTG GAGCACCAGGCAGTAAATTTCATCAACTTCACGGGTTCCACGGGGATCGGG GAGCGTATCGGTAAGATGGCTGGGATGCGTCCGATCATGTTGGAACTTGGC GGCAAGGATAGTGCGATTGTGCTGGAAGACGCAGACTTGGAATTGACAGCT AAAAACATTATCGCTGGAGCCTTCGGGTATAGTGGTCAACGTTGCACGGCA GTTAAGCGCGTTCTTGTTATGGAAAGTGTCGCGGATGAATTGGTCGAGAAG ATTCGCGAGAAAGTGTTAGCTCTTACGATTGGAAATCCAGAGGACGATGCT GACATCACTCCATTGATCGACACGAAATCCGCGGATTACGTCGAGGGGCTG ATCAACGACGCGAACGATAAGGGAGCAGCGGCTTTGACCGAGATCAAACGC GAGGGGAACCTGATCTGCCCGATTCTTTTTGACAAAGTCACAACTGACATGC GCTTGGCATGGGAAGAACCCTTCGGCCCAGTCTTGCCTATTATCCGCGTTAC TAGCGTAGAGGAAGCAATTGAAATTTCCAATAAATCCGAATATGGGTTGCA AGCGAGTATCTTTACTAACGATTTTCCACGTGCCTTTGGTATTGCGGAACAG TTAGAAGTCGGGACAGTTCACATCAACAACAAGACGCAGCGCGGGACAGAT AACTTCCCCTTTTTGGGAGCAAAGAAGTCTGGGGCTGGAATCCAAGGGGTG AAATACTCCATCGAAGCCATGACGACGGTGAAGAGCGTTGTTTTTGACATCA AGTAAAACATAAGGAGGAAAAACAGATGGCGAAACTGACCTCGGCGGTTCC GGTTCTGACGGCACGTGATGTGGCGGGCGCGGTTGAATTTTGGACGGATCGT CTGGGCTTCAGTCGTGATTTTGTGGAAGATGACTTCGCAGGCGTGGTTCGCG ATGACGTCACCCTGTTTATTTCCGCAGTTCAGGATCAAGTCGTGCCGGACAA CACGCTGGCTTGGGTGTGGGTTCGTGGCCTGGATGAACTGTATGCGGAATGG AGCGAAGTTGTCTCTACCAATTTCCGTGACGCGAGCGGTCCGGCCATGACGG AAATCGGCGAACAGCCGTGGGGTCGCGAATTTGCTCTGCGTGACCCGGCTG GCAACTGTGTCCATTTCGTGGCTGAAGAACAAGATTGAGTTGAGATGACACT GTGATCTAAAAAGAGCGACTTCGGTCGCTCTTTTTTTTACCTGA gapA-DAS + 4-zeoR 61 TCTACCGATTTCAACGGCGAAGTTTGCACTTCCGTGTTCGATGCTAAAGCTG GTATCGCTCTGAACGACAACTTCGTGAAACTGGTATCCTGGTACGACAACGA AACCGGTTACTCCAACAAAGTTCTGGACCTGATCGCTCACATCTCCAAAGCG GCCAACGATGAAAACTATTCTGAAAACTATGCGGATGCGTCTTGATCCTGAC GGATGGCCTTTTTGCGTTTCTACAAACTCTTTTTGTTTATTTTTCTAAATACAT TCAAATATGTATCCGCTCATGAGACAATAACCCTGATAAATGCTTCAATAAT ATTGAAAAAGGAAGAGTAATGGCGAAACTGACCTCGGCGGTTCCGGTTCTG ACGGCACGTGATGTGGCGGGCGCGGTTGAATTTTGGACGGATCGTCTGGGC TTCAGTCGTGATTTTGTGGAAGATGACTTCGCAGGCGTGGTTCGCGATGACG TCACCCTGTTTATTTCCGCAGTTCAGGATCAAGTCGTGCCGGACAACACGCT GGCTTGGGTGTGGGTTCGTGGCCTGGATGAACTGTATGCGGAATGGAGCGA AGTTGTCTCTACCAATTTCCGTGACGCGAGCGGTCCGGCCATGACGGAAATC GGCGAACAGCCGTGGGGTCGCGAATTTGCTCTGCGTGACCCGGCTGGCAAC TGTGTCCATTTCGTGGCTGAAGAACAAGATTGAGTTGAGATGACACTGTGAT CTAAAAAGAGCGACTTCGGTCGCTCTTTTTTTTACCTGATAAAATGAAGTTA AAGGACTGCGTCATGATTAAGAAAATTTTTGCCCTTCCGGTCATCGAACAAA TCTCCCCTGTCCTCTCCCGTCGTAAACTGGATGAACTGGACCTCATTGTGGTC GATCATCCCCAGGTAAAAGCCTCT gltA-DAS + 4-ampR 62 GTATTCCGTCTTCCATGTTCACCGTCATTTTCGCAATGGCACGTACCGTTGGC TGGATCGCCCACTGGAGCGAAATGCACAGTGACGGTATGAAGATTGCCCGT CCGCGTCAGCTGTATACAGGATATGAAAAACGCGACTTTAAAAGCGATATC AAGCGTGCGGCCAACGATGAAAACTATTCTGAAAACTATGCGGATGCGTCT TAATAGTCCTGACGGATGGCCTTTTTGCGTTTCTACAAACTCTTTTTGTTTAT TTTTCTAAATACATTCAAATATGTATCCGCTCATGAGACAATAACCCTGATA AATGCTTCAATAATATTGAAAAAGGAAGAGTATGAGTATTCAACATTTCCGT GTCGCCCTTATTCCCTTTTTTGCGGCATTTTGCCTTCCTGTTTTTGCTCACCCA GAAACGCTGGTGAAAGTAAAAGATGCTGAAGATCAGTTGGGTGCACGAGTG GGTTACATCGAACTGGATCTCAACAGCGGTAAGATCCTTGAGAGTTTTCGCC CCGAAGAACGTTTTCCAATGATGAGCACTTTTAAAGTTCTGCTATGTGGCGC GGTATTATCCCGTGTTGACGCCGGGCAAGAGCAACTCGGTCGCCGCATACA CTATTCTCAGAATGACTTGGTTGAGTACTCACCAGTCACAGAAAAGCATCTT ACGGATGGCATGACAGTAAGAGAATTATGCAGTGCTGCCATAACCATGAGT GATAACACTGCGGCCAACTTACTTCTGACAACGATCGGAGGACCGAAGGAG CTAACCGCTTTTTTGCACAACATGGGGGATCATGTAACTCGCCTTGATCGTT GGGAACCGGAGCTGAATGAAGCCATACCAAACGACGAGCGTGACACCACG ATGCCTACAGCAATGGCAACAACGTTGCGCAAACTATTAACTGGCGAACTA CTTACTCTAGCTTCCCGGCAACAATTAATAGACTGGATGGAGGCGGATAAA GTTGCAGGACCACTTCTGCGCTCGGCCCTTCCGGCTGGCTGGTTTATTGCTG ATAAATCTGGAGCCGGTGAGCGTGGGTCTCGCGGTATCATTGCAGCACTGG GGCCAGATGGTAAGCCCTCCCGTATCGTAGTTATCTACACGACGGGGAGTC AGGCAACTATGGATGAACGAAATAGACAGATCGCTGAGATAGGTGCCTCAC TGATTAAGCATTGGTAACTGTCAGACTAATGGTTGATTGCTAAGTTGTAAAT ATTTTAACCCGCCGTTCATATGGCGGGTTGATTTTTATATGCCTAAACACAA AAAATTGTAAAAATAAAATCCATTAACAGACCTATATAGATATTTAAAAAG AATAGAACAGCTCAAATTATCAGCAACCCAATACTTTCAATTAAAAACTTCA TGGTAGTCGCATTTATAACCCTATGAAA gltA-DAS + 4-purR 63 ACCGTCATTTTCGCAATGGCACGTACCGTTGGCTGGATCGCCCACTGGAGCG AAATGCACAGTGACGGTATGAAGATTGCCCGTCCGCGTCAGCTGTATACAG GATATGAAAAACGCGACTTTAAAAGCGATATCAAGCGTGCGGCCAACGATG AAAACTATTCTGAAAACTATGCGGATGCGTCTTAATCCTGACGGATGGCCTT TTTGCGTTTCTACAAACTCTTTTTGTTTATTTTTCTAAATACATTCAAATATGT ATCCGCTCATGAGACAATAACCCTGATAAATGCTTCAATAATATTGAAAAA GGAAGAGTATGACTGAATACAAGCCCACGGTACGCTTGGCGACGCGCGACG ATGTTCCCCGCGCTGTTCGTACATTAGCTGCGGCCTTTGCAGATTACCCAGC GACGCGCCATACGGTCGATCCGGACCGCCATATCGAGCGTGTCACAGAATT GCAGGAACTTTTCTTAACTCGCGTGGGCCTTGACATCGGAAAGGTCTGGGTG GCTGACGATGGCGCTGCAGTGGCTGTTTGGACCACTCCGGAGAGTGTAGAG GCTGGTGCAGTGTTCGCCGAAATTGGTCCTCGTATGGCCGAATTAAGTGGAA GTCGTCTGGCAGCCCAACAACAAATGGAAGGGTTGCTTGCGCCCCACCGTC CGAAAGAACCCGCGTGGTTCCTTGCCACCGTTGGAGTAAGCCCAGATCACC AGGGGAAGGGTTTAGGATCTGCCGTAGTTTTACCAGGTGTGGAGGCAGCAG AACGTGCGGGAGTTCCGGCCTTCCTTGAGACGTCGGCGCCGCGCAATTTACC GTTTTACGAACGTCTTGGATTCACCGTTACGGCGGACGTGGAGGTGCCGGAG GGACCCCGTACTTGGTGTATGACTCGTAAACCGGGAGCCTGATAATGGTTGA TTGCTAAGTTGTAAATATTTTAACCCGCCGTTCATATGGCGGGTTGATTTTTA TATGCCTAAACACAAAAAATTGTAAAAATAAAATCCATTAACAGACCTATA TAGATATTTAAAAAGAATAGAACAGCTCAAATTATCAGCAACCCA gltA-DAS + 4-zeoR 64 GTATTCCGTCTTCCATGTTCACCGTCATTTTCGCAATGGCACGTACCGTTGGC TGGATCGCCCACTGGAGCGAAATGCACAGTGACGGTATGAAGATTGCCCGT CCGCGTCAGCTGTATACAGGATATGAAAAACGCGACTTTAAAAGCGATATC AAGCGTGCGGCCAACGATGAAAACTATTCTGAAAACTATGCGGATGCGTCT TAATAGTTGACAATTAATCATCGGCATAGTATATCGGCATAGTATAATACGA CTCACTATAGGAGGGCCATCATGGCCAAGTTGACCAGTGCCGTTCCGGTGCT CACCGCGCGCGACGTCGCCGGAGCGGTCGAGTTCTGGACCGACCGGCTCGG GTTCTCCCGGGACTTCGTGGAGGACGACTTCGCCGGTGTGGTCCGGGACGAC GTGACCCTGTTCATCAGCGCGGTCCAGGACCAGGTGGTGCCGGACAACACC CTGGCCTGGGTGTGGGTGCGCGGCCTGGACGAGCTGTACGCCGAGTGGTCG GAGGTCGTGTCCACGAACTTCCGGGACGCCTCCGGGCCGGCCATGACCGAG ATCGGCGAGCAGCCGTGGGGGCGGGAGTTCGCCCTGCGCGACCCGGCCGGC AACTGCGTGCACTTTGTGGCAGAGGAGCAGGACTGAGGATAAGTAATGGTT GATTGCTAAGTTGTAAATATTTTAACCCGCCGTTCATATGGCGGGTTGATTTT TATATGCCTAAACACAAAAAATTGTAAAAATAAAATCCATTAACAGACCTA TATAGATATTTAAAAAGAATAGAACAGCTCAAATTATCAGCAACCCAATAC TTTCAATTAAAAACTTCATGGTAGTCGCATTTATAACCCTATGAAA lpd-DAS + 4-gentR 65 GCGGCGAGCTGCTGGGTGAAATCGGCCTGGCAATCGAAATGGGTTGTGATG CTGAAGACATCGCACTGACCATCCACGCGCACCCGACTCTGCACGAGTCTGT GGGCCTGGCGGCAGAAGTGTTCGAAGGTAGCATTACCGACCTGCCGAACCC GAAAGCGAAGAAGAAGGCGGCCAACGATGAAAACTATTCTGAAAACTATG CGGATGCGTCTTAATAGCGAATCCATGTGGGAGTTTATTCTTGACACAGATA TTTATGATATAATAACTGAGTAAGCTTAACATAAGGAGGAAAAACATATGT TACGCAGCAGCAACGATGTTACGCAGCAGGGCAGTCGCCCTAAAACAAAGT TAGGTGGCTCAAGTATGGGCATCATTCGCACATGTAGGCTCGGCCCTGACCA AGTCAAATCCATGCGGGCTGCTCTTGATCTTTTCGGTCGTGAGTTCGGAGAC GTAGCCACCTACTCCCAACATCAGCCGGACTCCGATTACCTCGGGAACTTGC TCCGTAGTAAGACATTCATCGCGCTTGCTGCCTTCGACCAAGAAGCGGTTGT TGGCGCTCTCGCGGCTTACGTTCTGCCCAAGTTTGAGCAGCCGCGTAGTGAG ATCTATATCTATGATCTCGCAGTCTCCGGCGAGCACCGGAGGCAGGGCATTG CCACCGCGCTCATCAATCTCCTCAAGCATGAGGCCAACGCGCTTGGTGCTTA TGTGATCTACGTGCAAGCAGATTACGGTGACGATCCCGCAGTGGCTCTCTAT ACAAAGTTGGGCATACGGGAAGAAGTGATGCACTTTGATATCGACCCAAGT ACCGCCACCTAATTTTTCGTTTGCCGGAACATCCGGCAATTAAAAAAGCGGC TAACCACGCCGCTTTTTTTACGTCTGCAATTTACCTTTCCAGTCTTCTTGCTC CACGTTCAGAGAGACGTTCGCATACTGCTGACCGTTGCTCGTTATTCAGCCT GACAGTATGGTTACTGTC udhA-DAS + 4-bsdR 66 TCTGGGTATTCACTGCTTTGGCGAGCGCGCTGCCGAAATTATTCATATCGGT CAGGCGATTATGGAACAGAAAGGTGGCGGCAACACTATTGAGTACTTCGTC AACACCACCTTTAACTACCCGACGATGGCGGAAGCCTATCGGGTAGCTGCG TTAAACGGTTTAAACCGCCTGTTTGCGGCCAACGATGAAAACTATTCTGAAA ACTATGCGGATGCGTCTTAATAGTTGACAATTAATCATCGGCATAGTATATC GGCATAGTATAATACGACTCACTATAGGAGGGCCATCATGAAGACCTTCAA CATCTCTCAGCAGGATCTGGAGCTGGTGGAGGTCGCCACTGAGAAGATCAC CATGCTCTATGAGGACAACAAGCACCATGTCGGGGCGGCCATCAGGACCAA GACTGGGGAGATCATCTCTGCTGTCCACATTGAGGCCTACATTGGCAGGGTC ACTGTCTGTGCTGAAGCCATTGCCATTGGGTCTGCTGTGAGCAACGGGCAGA AGGACTTTGACACCATTGTGGCTGTCAGGCACCCCTACTCTGATGAGGTGGA CAGATCCATCAGGGTGGTCAGCCCCTGTGGCATGTGCAGAGAGCTCATCTCT GACTATGCTCCTGACTGCTTTGTGCTCATTGAGATGAATGGCAAGCTGGTCA AAACCACCATTGAGGAACTCATCCCCCTCAAGTACACCAGGAACTAAAGTA AAACTTTATCGAAATGGCCATCCATTCTTGCGCGGATGGCCTCTGCCAGCTG CTCATAGCGGCTGCGCAGCGGTGAGCCAGGACGATAAACCAGGCCAATAGT GCGGCGTGGTTCCGGCTTAATGCACGG zwf-DAS + 4-bsdR 67 GAAGTGGAAGAAGCCTGGAAATGGGTAGACTCCATTACTGAGGCGTGGGCG ATGGACAATGATGCGCCGAAACCGTATCAGGCCGGAACCTGGGGACCCGTT GCCTCGGTGGCGATGATTACCCGTGATGGTCGTTCCTGGAATGAGTTTGAGG CGGCCAACGATGAAAACTATTCTGAAAACTATGCGGATGCGTCTTAATAGTT GACAATTAATCATCGGCATAGTATATCGGCATAGTATAATACGACTCACTAT AGGAGGGCCATCATGAAGACCTTCAACATCTCTCAGCAGGATCTGGAGCTG GTGGAGGTCGCCACTGAGAAGATCACCATGCTCTATGAGGACAACAAGCAC CATGTCGGGGCGGCCATCAGGACCAAGACTGGGGAGATCATCTCTGCTGTC CACATTGAGGCCTACATTGGCAGGGTCACTGTCTGTGCTGAAGCCATTGCCA TTGGGTCTGCTGTGAGCAACGGGCAGAAGGACTTTGACACCATTGTGGCTGT CAGGCACCCCTACTCTGATGAGGTGGACAGATCCATCAGGGTGGTCAGCCC CTGTGGCATGTGCAGAGAGCTCATCTCTGACTATGCTCCTGACTGCTTTGTG CTCATTGAGATGAATGGCAAGCTGGTCAAAACCACCATTGAGGAACTCATC CCCCTCAAGTACACCAGGAACTAAAGTAATATCTGCGCTTATCCTTTATGGT TATTTTACCGGTAACATGATCTTGCGCAGATTGTAGAACAATTTTTACACTTT CAGGCCTCGTGCGGATTCACCCACGAGGCTTTTTTTATTACACTGACTGAAA CGTTTTTGCCCTATGAGCTCCGGTTACAGGCGTTTCAGTCATAAATCCTCTGA ATGAAACGCGTTGTGAATC dadX-DAS + 4-purR 68 GCGTGCGCACCATGACGGTGGGGACCGTCTCGATGGATATGCTAGCGGTCG ATTTAACGCCTTGCCCGCAGGCGGGTATTGGTACGCCGGTTGAGCTGTGGGG CAAGGAGATCAAAATTGATGATGTCGCCGCCGCTGCCGGAACGGTGGGCTA TGAGTTGATGTGCGCGCTGGCGCTACGCGTCCCGGTTGTGACGGTGGCGGCC AACGATGAAAACTATTCTGAAAACTATGCGGATGCGTCTTAATCCTGACGG ATGGCCTTTTTGCGTTTCTACAAACTCTTTTTGTTTATTTTTCTAAATACATTC AAATATGTATCCGCTCATGAGACAATAACCCTGATAAATGCTTCAATAATAT TGAAAAAGGAAGAGTATGACTGAATACAAGCCCACGGTACGCTTGGCGACG CGCGACGATGTTCCCCGCGCTGTTCGTACATTAGCTGCGGCCTTTGCAGATT ACCCAGCGACGCGCCATACGGTCGATCCGGACCGCCATATCGAGCGTGTCA CAGAATTGCAGGAACTTTTCTTAACTCGCGTGGGCCTTGACATCGGAAAGGT CTGGGTGGCTGACGATGGCGCTGCAGTGGCTGTTTGGACCACTCCGGAGAG TGTAGAGGCTGGTGCAGTGTTCGCCGAAATTGGTCCTCGTATGGCCGAATTA AGTGGAAGTCGTCTGGCAGCCCAACAACAAATGGAAGGGTTGCTTGCGCCC CACCGTCCGAAAGAACCCGCGTGGTTCCTTGCCACCGTTGGAGTAAGCCCA GATCACCAGGGGAAGGGTTTAGGATCTGCCGTAGTTTTACCAGGTGTGGAG GCAGCAGAACGTGCGGGAGTTCCGGCCTTCCTTGAGACGTCGGCGCCGCGC AATTTACCGTTTTACGAACGTCTTGGATTCACCGTTACGGCGGACGTGGAGG TGCCGGAGGGACCCCGTACTTGGTGTATGACTCGTAAACCGGGAGCCTGAT AACTTGTTGTAAGCCGGATCGGAGGCAACGTCTTCTGGGTGCAAAAAAATC ATCCATCCGGCTGGTCAGCAACTGTAGTTGTTAATGTGACAGAGCCATTGCC CATGATAGTGTCCATTAAAAGGATGGACACTATTTCCCCGGAACCTGAACTC ACCGCACAGGCGTTCTACATAAAACGCTTACGCTTCATTGTTGACTC

Section 4: Dynamic Control Over Protein Levels.

Plasmids expressing fluorescent proteins and silencing guides were transformed into the corresponding hosts strain listed in Table 8. Strains were evaluated in triplicate in an m2p-labs Biolector™, which simultaneously measures fluorescence including GFPuv and mCherry levels, as well as biomass levels.

TABLE 8 Strains used for Dynamic Control over protein levels Synthetic Metabolic Microbe Valves Plasmid Host Strain E. coli RFP-control pCDF-mcherry1 + DLF_0002 pSMART-IN:yibDp-GFPuv Proteolysis pCDF-mcherry2 + DLF_0025 pSMART-IN:yibDp-GFPuv Silencing pCDF-mcherry1 + DLF_01517 pCASCADE-proD + pSMART-IN:yibDp-GFPuv Proteolysis + pCDF-mcherry2 + DLF_0025 Silencing pCASCADE-proD + pSMART-IN:yibDp-GFPuv

OD600 readings were corrected using the formula below, where OD600 refers to an offline measurement. OD600* refers to Biolector biomass reading, 10 indicates the start point, and if indicates the final point.

Proteolysis + Silencing pCDF-mcherry2 + DLF_0025 pCASCADE-proD + pSMART-IN:yibDp-GFPuv

OD600 readings were corrected using the formula below, where OD600 refers to an offline measurement, OD600* refers to Biolector biomass reading, t0 indicates the start point, and tf indicates the final point.

$\begin{matrix} {{0D\; 600_{t}} = {{\left( {{0D\; 600_{t}^{*}} - {0D\; 600_{t\; 0}^{*}}} \right)*\frac{\left( {{0D\; 600_{tf}} - {0D\; 600_{t\; 0}}} \right)}{\left( {{0D\; 600_{tf}^{*}} - {0D\; 600_{t\; 0}^{*}}} \right)}} + 0.25}} & {{Equation}\mspace{14mu} {S1}} \end{matrix}$

Section 5: Metabolic Control

Near Equilibrium Reactions

The impact of Valves on metabolite pools for near equilibrium reactions is illustrated using the G6P node as an example. Abbreviations: Gluc, glucose, G6P, glucose-6-phosphate; F6P, fructose-6-phosphate; 6PGl, 6-phosphate-gluconolactone.

G6P Node without Valves

$\begin{matrix} {{{Steady}\mspace{14mu} {State}\mspace{14mu} {Mass}\mspace{14mu} {balance}\mspace{14mu} J_{1}} = {J_{2} + J_{3}}} & {{Equation}\mspace{14mu} {S2}} \\ {{{Net}\mspace{14mu} {Flux}} = {J_{t} = {e^{\frac{- {dG}}{RT}} - 1}}} & {{Equation}\mspace{14mu} {S3}} \\ {{e^{\frac{- {dG}}{RT}} - 1} = {e^{\frac{{- {dG}}\; 2}{RT}} - 1 + e^{\frac{{- {dG}}\; 3}{RT}} - 1}} & {{Equation}\mspace{14mu} {S4}} \\ {e^{\frac{- {dG}}{RT}} = {e^{\frac{{- {dG}}\; 2}{RT}} + e^{\frac{{- {dG}}\; 3}{RT}} - 1}} & {{Equation}\mspace{14mu} {S5}} \\ {{{Keq}\; 1} = {{{Keq}\; 2} + {{Keq}\; 3} - 1}} & {{Equation}\mspace{14mu} {S6}} \\ {{{{Keq}\; 1} + 1} = {{{Keq}\; 2} + {{Keq}\; 3}}} & {{Equation}\mspace{14mu} {S7}} \\ {{\frac{\left\lbrack {G\; 6P} \right\rbrack}{\lbrack{Gluc}\rbrack} + 1} = {\frac{\left\lbrack {F\; 6P} \right\rbrack}{\left\lbrack {G\; 6P} \right\rbrack} + \frac{\left\lbrack {6{PGl}} \right\rbrack}{\left\lbrack {G\; 6P} \right\rbrack}}} & {{Equation}\mspace{14mu} {S8}} \\ {{\frac{\left\lbrack {G\; 6P} \right\rbrack}{\lbrack{Gluc}\rbrack} + 1} = \frac{\left\lbrack {F\; 6P} \right\rbrack + \left\lbrack {6{PGl}} \right\rbrack}{\left\lbrack {G\; 6P} \right\rbrack}} & {{Equation}\mspace{14mu} {S9}} \\ {{\frac{\left\lbrack {G\; 6P} \right\rbrack^{2}}{\lbrack{Gluc}\rbrack} + \left\lbrack {G\; 6P} \right\rbrack} = {\left\lbrack {F\; 6P} \right\rbrack + \left\lbrack {6{PGl}} \right\rbrack}} & {{Equation}\mspace{14mu} {S10}} \\ {\left\lbrack {F\; 6P} \right\rbrack = {\frac{\left\lbrack {G\; 6P} \right\rbrack^{2}}{\lbrack{Gluc}\rbrack} + \left\lbrack {G\; 6P} \right\rbrack - \left\lbrack {6{PGl}} \right\rbrack}} & {{Equation}\mspace{14mu} {S11}} \end{matrix}$

G6P Node with Valves

When zwf valve is in effect, J₃≈0.

$\begin{matrix} {{{Steady}\mspace{14mu} {State}\mspace{14mu} {Mass}\mspace{14mu} {balance}\mspace{14mu} J_{1}} = J_{2}} & {{Equation}\mspace{14mu} {S12}} \\ {{{Net}\mspace{14mu} {Flux}} = {J_{i} = {e^{\frac{- {dG}}{RT}} - 1}}} & {{Equation}\mspace{14mu} {S13}} \\ {{e^{\frac{- {dG}}{RT}} - 1} = {e^{\frac{{- {dG}}\; 2}{RT}} - 1}} & {{Equation}\mspace{14mu} {S14}} \\ {{{Keq}\; 1} = {{Keq}\; 2}} & {{Equation}\mspace{14mu} {S15}} \\ {\frac{\left\lbrack {G\; 6P} \right\rbrack}{\lbrack{Gluc}\rbrack} = \frac{\left\lbrack {F\; 6P} \right\rbrack}{\left\lbrack {G\; 6P} \right\rbrack}} & {{Equation}\mspace{14mu} {S16}} \\ {\left\lbrack {F\; 6P} \right\rbrack = \frac{\left\lbrack {G\; 6P} \right\rbrack^{2}}{\lbrack{Gluc}\rbrack}} & {{Equation}\mspace{14mu} {S17}} \end{matrix}$

Impact of Valves

$\begin{matrix} {{\left\lbrack {F\; 6P} \right\rbrack {network}} = {\frac{\left\lbrack {G\; 6P} \right\rbrack^{2}}{\lbrack{Gluc}\rbrack} + \left\lbrack {G\; 6P} \right\rbrack - \left\lbrack {6{PGl}} \right\rbrack}} & {{Equation}\mspace{14mu} {S11}} \\ {{\left\lbrack {F\; 6P} \right\rbrack {valve}} = \frac{\left\lbrack {G\; 6P} \right\rbrack^{2}}{\lbrack{Gluc}\rbrack}} & {{Equation}\mspace{14mu} {S17}} \\ {{{Since}\mspace{14mu} {close}\mspace{14mu} {to}\mspace{14mu} {{equilibrium}\;\left\lbrack {6{PGl}} \right\rbrack}} > \left\lbrack {G\; 6P} \right\rbrack} & \; \\ {{\left\lbrack {F\; 6P} \right\rbrack {valve}}>={\left\lbrack {F\; 6P} \right\rbrack {network}}} & \; \\ {{{The}\mspace{14mu} {removal}\mspace{14mu} {of}\mspace{14mu} {thermodynamically}\mspace{14mu} {favored}}{{reactions}\mspace{14mu} {near}\mspace{14mu} {equilibrium}\mspace{14mu} {from}\mspace{14mu} {the}\mspace{14mu} {network}\mspace{14mu} {will}}{{result}\mspace{14mu} {in}\mspace{14mu} {increased}\mspace{14mu} {metabolite}\mspace{14mu} {{pools}.}}} & \; \end{matrix}$

Section 6: Gene Silencing Arrays & Pathway Expression Constructs

The design and construction of CASCADE guides and guide arrays is illustrated below in FIG. 14 and FIG. 15A-B. The pCASCADE-control plasmid was prepared by swapping the pTet promoter in pcrRNA.Tet u with an insulated low phosphate induced ugpB promoter⁸². Two promoters were responsible for regulating gltA gene, and sgRNA was designed for both promoters, resulting in guide gltA1 (G1) and gltA2 (G2).⁸⁹ Four promoters were responsible for regulating gapA gene, and sgRNA was designed for the first promoter, since during exponential phase of growth, gapA mRNAs were mainly initiated at the highly efficient gapA P1 promoter and remained high during stationary phase compared to the other three gapA promoters.⁹⁰ Multiple promoters upstream of lpd gene were involved in lpd regulation (https://ecocyc.org.gene?orgid=ECOLI&id=EG10543#tab=showAll), thus design of unique and effective sgRNA for lpd only was not possible. Promoter sequences for fabI, udhA and zwf were obtained from EcoCyc database (https://ecocyc.org/). To design CASCADE guide array, CASCADE PAM sites near the −35 or −10 box of the promoter of interest were identified, 30 bp at the 3′ end of PAM site was selected as the guide sequence and cloned into pCASCADE plasmid using Q5 site-directed mutagenesis (NEB, MA) following manufacturer's protocol, with the modification that 5% v/v DMSO was added to the Q5 PCR reaction. The pCASCADE-control vector was used as template. pCASCADE plasmids with arrays of two or more guides were prepared as illustrated in FIG. 15A-B. The pCASCADE guide array plasmid was prepared by sequentially amplifying complementary halves of each smaller guide plasmid by PCR, followed by subsequent DNA assembly. Table 9 lists sgRNA guide sequences and primers used to construct them. All pCASCADE silencing plasmids are listed in Table 10 below and are available at Addgene.

TABLE 9 List of sgRNA guide sequences and primers used to construct them. Spacers are italicized. sgRNA/Primer Name Sequence SEQ ID NO Template fabI TCGAGTTCCCCGCGCCAGCGGG 69 GATAAACCGTTGATTATAATAA CCGTTTATCTGTTCGTATCGAG TTCCCCGCGCCAGCGGGGATAA ACCG fabI-FOR GTTTATCTGTTCGTATCGAGTT 70 pCASCADE control CCCCGCGCCAGCGGGGATAAAC CGAAAAAAAAACCCC fabI-REV GGTTATTATAATCAACCTGTTTA 71 TCCCCGCTGGCCTCGGGGAACT CGAGGTGGTACCAGATC gapAP1 TCGAGTTCCCCGCGCCAGCGGG 72 GATAAACCGGTTTTTGTAATTTT ACAGGCAACCTTTTATTCGAGT TCCCCGCGCCAGCGGGGATAAA CCG gapAP1-FOR CAGGCAACCTTTTATTCGAGTT 73 pCASCADE control CCCCGCGCCAGCGGGGATAAAC CGAAAAAAAAACCCC gapAP1-REV TAAAATTACAAAAACCGGTTT 74 ATCCCCGCTGGCGCGGGGAAC TCGAGGTGGTACCAGATC gltA1 TCGAGTTCCCCGCGCCAGCGGG 75 GATAAACCGAAAAGCATATAAT GCGTAAAAGTTATGAAGTTCG AGTTCCCCGCGCCAGCGGGGAT AAACCG gltA1-FOR GCGTAAAAGTTATGAAGTTCG 76 pCASCADE control AGTTCCCCGCGCCAGCGGGGAT AAACCGAAAAAAAAACCCC gltA1-REV ATTATATGCTTTTCGGTTTATC 77 CCCGCTGGCGCGCTGCTAACTCG AGGTGGTACCAGATCT gltA2 TCGAGTTCCCCGCGCCAGCGGG 78 GATAAACCGTATTGACCAATTC ATTCGGGACAGTTATTAGTTCG AGTTCCCCGCGCCAGCGGGGAT AAACCG gltA2-FOR GGGACAGTTATTAGTTCGAGTT 79 pCASCADE control CCCCGCGCCAGCGGGGATAAAC CGAAAAAAAAACCCC gltA2-REV GAATGAATTGGTCAATACGGT 80 TTATCCCCGCTGGCGCGGGGA ACTCGAGGTGGTACCAGATCT proD TCGAGTTCCCCGCGCCAGCGGG 81 GATAAACCGAGTGGTTGCTGGA TAACTTTACGGGCATGCTCGAG TTCCCCGCGCCAGCGGGGATAA ACCG proD-FOR AACTTTACGGGCATGCTCGAGT 82 pCASCADE control TCCCCGCGCCAGCGGGGATAAA CCGAAAAAAAAAACCCC proD-REV ATCCAGCAACCACTCGGTTTAT 83 CCCCGCTGGCGCGGGGAACTC GAGGTGGTACCAGATCT udhA TCGAGTTCCCCGCGCCAGCGGG 84 GATAAACCGTTACCATTCTGTT GCTTTTATGTATAAGAATCGAG TTCCCCGCGCCAGCGGGGATAA ACCG udhA-FOR TTTTATGTATAAGAATCGAGTT 85 pCASCADE control CCCCGCGCCAGCGGGGATAAAC CGAAAAAAAAACCCC udhA-REV GCAACAGAATGGTAACGGTT 86 ATCCCCGCTGGCGCGGGGAAC TCGAGGTGGTACCAGATC zwf TCGAGTTCCCCGCGCCAGCGGG 87 GATAAACCGCTCGTAAAAGCAG TACAGTGCACCGTAAGATCGA GTTCCCCGCGCCAGCGGGGATA AACCG zwf-FOR CAGTGCACCGTAAGATCGAGTT 88 pCASCADE control CCCCGCGCCAGCGGGGATAAAC CGAAAAAAAAACCCC zwf-REV TACTGCTTTTACGAGCGGTTTA 89 TCCCCGCTGGCGCGGGGAACT CGAGGTGGTACCAGATC FG1 TCGAGTTCCCCGCGCCAGCGGG 90 GATAAACCGTTGATTATAATAA CCGTTTATCTGTTCGTATCGAG TTCCCCGCGCCAGCGGGGATAA ACCGAAAAGCATATAATGCGT AAAAGTTATGAAGTTCGAGTTC CCCGCGCCAGCGGGGATAAACC G gltA1-FOR GCGCCAGCGGGGATAAACCGA 91 pCASCADE-gltA1 AAAGCATATAATGCG pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 92 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 93 pCASCADE-fab1 GGGCAAG fab1-REV CGGTTTATCCCCGCTGGCGCG 94 GGGAACTCGATACGAACAGAT AAACGGTTATTATAATC FG2 TCGAGTTCCCCGCGCCAGCGGG 95 GATAAACCGTTGATTATAATAA CCGTTTATCTGTTCGTATCGAG TTCCCCGCGCCAGCGGGGATAA ACCGTATTGACCAATTCATTCG GGACAGTTATTAGTTCGAGTTC CCCGCGCCAGCGGGGATAAACC G gltA2-FOR GCGCCAGCGGGGATAAACCGT 96 pCASCADE-gltA2 ATTGACCAATTCATTC pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 97 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 98 pCASCADE-fabI GGGCAAG fabI-REV CGGTTTATCCCCGCTGGCGCG 99 GGGAACTCGATACGAACAGAT AAACGGTTATTATAATC FU TCGAGTTCCCCGCGCCAGCGGG 100 GATAAACCGTTTGATTATAATAA CCGTTTATCTGTTCGTATCGAG TTCCCCGCGCCAGCGGGGATAA ACCGTTACCATTCTGTTGCTTT TATGTATAAGAATCGAGTTCCC CGCGCCAGCGGGGATAAACCG udhA-FOR GCGCCAGCGGGGATAAACCGT 101 pCASCADE-udhA TACCATTCTGTTG pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 102 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 103 pCASCADE-fabI GGGCAAG fabI-REV CGGTTTATCCCCGCTGGCGCG 104 GGGAACTCGATACGAACAGAT AAACGGTTATTATAATC FZ TCGAGTTCCCCGCGCCAGCGGG 105 GATAAACCGTTGATTATAATAA CCGTTTATCTGTTCGTATCGAG TTCCCCGCGCCAGCGGGGATAA ACCGCTCGTAAAAGCAGTACA GTGCACCGTAAGATCGAGTTCC CCGCGCCAGCGGGGATAAACCG zwf-FOR GCGCCAGCGGGGATAAACCGC 106 pCASCADE-zwf TCGTAAAAG pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 107 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 108 pCASCADE-fabI GGGCAAG fabI-REV CGGTTTATCCCCGCTGGCGCG 109 GGGAACTCGATACGAACAGAT AAACGGTTATTATAATC G1G2 TCGAGTTCCCCGCGCCAGCGGG 110 GATAAACCGAAAAGCATATAAT GCGTAAAAGTTATGAAGTTCG AGTTCCCCGCGCCAGCGGGGAT AAACCGTATTGACCAATTCATT CGGGACAGTTATTAGTTCGAGT TCCCCGCGCCAGCGGGGATAAA CCG gltA2-FOR GCGCCAGCGGGGATAAACCGT 111 pCASCADE-gltA2 ATTGACCAATTCATTC pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 112 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 113 pCASCADE-gltA1 GGGCAAG gltA1-REV CGGTTTATCCCCGCTGGCGCG 114 GGGAACTCGAACTTCATAACT TTTAC G1U TCGAGTTCCCCGCGCCAGCGGG 115 GATAAACCGAAAAGCATATAATG CGTAAAAGTTATGAAGTTCGA GTTCCCCGCGCCAGCGGGGATA AACCGTTACCATTCTGTTGCTT TTATGTATAAGAATCGAGTTCC CCGCGCCAGCGGCTGATAAACCG udhA-FOR GCGCCAGCGGGGATAAACCGT 116 pCASCADE-udhA TACCATTCTGTTG pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 117 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 118 pCASCADE-gltA1 GGGCAAG gltA1-REV CGGTTTATCCCCGCTGGCGCG 119 GGGAACTCGAACTTCATAACT TTTAC G1Z TCGAGTTCCCCGCGCCAGCGGG 120 GATAAACCGAAAAGCATATAAT GCGTAAAAGTTATGAAGTTCG AGTTCCCCGCGCCAGCGGGGAT AAACCGCTCGTAAGCAGTA CAGTGCACCGTAAGATCGAGTT CCCCGCGCCAGCGGGGATAAAC CG zwf-FOR GCGCCAGCGGGGATAAACCGC 121 pCASCADE-zwf TCGTAAAAG pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 122 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 123 pCASCADE-gltAQ TGGCAAG gltAQ-REV CGGTTTATCCCCGCTGGCGCG 124 GGGAACTCGAACTTCATAACT TTTAC G2U TCGAGTTCCCCGCGCCAGCGGG 125 GATAAACCGTATTGACCAATTCA TTCGGGACAGTTATTAGTTCGA GTTCCCCGCGCCAGCGGGGATA AACCGTTACCATTCTGTTGCTT TTATGTATAAGAATCGAGTTCC CCGCGCCAGCGGGGATAAACCG udhA-FOR GCGCCAGCGGGGATAAACCGT 126 pCASCADE-udhA TACCATTCTGTTG pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 127 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 128 pCASCADE-gltA2 GGGCAAG gltA2-REV CGGTTTATCCCCGCTGGCGCG 129 GGGAACTCGAACTAATAACTG TC G2Z TCGAGTTCCCCGCGCCAGCGGG 130 GATAAACCGTATTGACCAATTCA TTCGGGACAGTTATTAGTTCGA GTTCCCCGCGCCAGCGGGGATA AACCGCTCGTAAAAGCAGTAC AGTGCACCGTAAGATCGAGTTC CCCGCGCCAGCGGGGATAAACC G zwf-FOR GCGCCAGCGGGGATAAACCGC 131 pCASCADE-zwf TCGTAAAAG pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 132 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 133 pCASCADE-gltA2 GGGCAAG gltA2-REV CGGTTTATCCCCGCTGGCGCG 134 GGGAACTCGAACTAATAACTG TC UZ TCGAGTTCCCCGCGCCAGCGGG 135 GATAAACCGTTACCATTCTGTT GCTTTTATGTATAAGAATCGAG TTCCCCGCGCCAGCGGGGATAA ACCGCTCGTAAAAGCAGTACA GTGCACCGTAAGATCGAGTTCC CCGCGCCAGCGGGGATAAACCG zwf-FOR GCGCCAGCGGGGATAAACCGC 136 pCASCADE-zwf TCGTAAAAG pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 137 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 138 pCASCADE-udhA GGGCAAG udhA-REV CGGTTTATCCCCGCTGGCGCG 139 GGGAACTCGATTCTTATACAT AAAAGC FG1G2 TCGAGTTCCCCGCGCCAGCGGG 140 GATAAACCGTTGATTATAATAA CCGTTTATCTGTTCGTATCGAG TTCCCCGCGCCAGCGGGGATAA ACCGAAAAGCATATAATGCGT AAAAGTTATGAAGTTCGAGTTC CCCGCGCCAGCGGGGATAAACC GTATTGACCAATTCAATTCGGG ACAGTTATTAGTTCGAGTTCCC CGCGCCAGCGGGGATAAACCG gltA2-FOR GCGCCAGCGGGGATAAACCGT 141 pCASCADE-gltA2 ATTGACCAATTCATTC pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 142 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 143 pCASCADE-FG1 GGGCAAG gltA1-REV CGGTTTATCCCCGCTGGCGCG 144 GGGAACTCGAACTTCATAACT TTTAC G1G2A TCGAGTTCCCCGCGCCAGCGGG 145 GATAAACCGAAAAGCATATAAT GCGTAAAAGTTATGAAGTTCG AGTTCCCCGCGCCAGCGGGGAT AAACCGTATTGACCAATTCATT CGGGACAGTTATTAGTTCGAGT TCCCCGCGCCAGCGGGGATAAA CCGGTTTTTGTAATTT TACAGG CAACCTTTTATTCGAGTTCCCC GCGCCAGCGGGGATAAACCG gapAP1-FOR GCGCCAGCGGGGATAAACCGG 146 pCASCADE-gapAP1 TTTTTGTAATTT TACAGGC pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 147 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 148 pCASCADE-G1G2 GGGCAAG gltA2-REV CGGTTTATCCCCGCTGGCGCG 149 GGGAACTCGAACTAATAACTG TC G1G2U TCGAGTTCCCCGCGCCAGCGGG 150 GATAAACCGAAAAGCATATAAT GCGTAAAAGTTATGAAGTTCG AGTTCCCCGCGCCAGCGGGGAT AAACCGTATTGACCAATTCATT CGGGACAGTTATTAGTTCGAGT TCCCCGCGCCAGCGGGGATAAA CCGTTACCATTCTGTTGCTTTT ATGTATAAGAATCGAGTTCCCC GCGCCAGCGGGGATAAACCG udhA-FOR GCGCCAGCGGGGATAAACCGT 151 pCASCADE-udhA TACCATTCTGTTG pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 152 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 153 pCASCADE-G1G2 GGGCAAG gltA2-REV CGGTTTATCCCCGCTGGCGCG 154 GGGAACTCGAACTAATAACTG TC G1G2Z TCGAGTTCCCCGCGCCAGCGGG 155 GATAAACCGAAAAGCATATAAT GCGTAAAAGTTATGAAGTTCG AGTTCCCCGCGCCAGCGGGGAT AAACCGTATTGACCAATTCATT CGGGACAGTTATTAGTTCGAGT TCCCCGCGCCAGCGGGGATAAA CCGCTCGTAAAAGCAGTACAG TGCACCGTAAGATCGAGTTCCC CGCGCCAGCGGGGATAAACCG zwf-FOR GCGCCAGCGGGGATAAACCGC 156 pCASCADE-zwf TCGTAAAAG pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 157 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 158 pCASCADE-G1G2 GGGCAAG gltA2-REV CGGTTTATCCCCGCTGGCGCG 159 GGGAACTCGAACTAATAACTG TC FG1G2A TCGAGTTCCCCGCGCCAGCGGG 160 GATAAACCGTTGATTATAATAA CCGTTTATCTGTTCGTATCGAG TTCCCCGCGCCAGCGGGGATAA ACCGAAAAGCATATAATGCGT AAAAGTTATGAAGTTCGAGTTC CCCGCGCCAGCGGGGATAAACC GTATTGACCAATTCATTCGGG ACAGTTATTAGTTCGAGTTCCC CGCGCCAGCGGGGATAAACCGG TTTTTGTAATTT TACAGGCAAC CTTTTATTCGAGTTCCCCGCGC CAGCGGGGATAAACCG gapAP1-FOR GCGCCACTCGGGGATAAACCGG 161 pCASCADE-gapAP1 TTTTTGTAATTT TACAGGC pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 162 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCACTGC 163 pCASCADE-FG1G2 GGGCAAG gltA2-REV CGGTTTATCCCCGCTGGCGCG 164 CTGGAACTCGAACTAATAACTG TC FG1G2U TCGAGTTCCCCGCGCCAGCGGG 165 GATAAACCGTTGATTATAATAA CCGTTTATCTGTTCGTATCGAG TTCCCCGCGCCAGCGGGGATAA ACCGAAAAGCATATAATGCGT AAAAGTTATGAAGTTCGAGTTC CCCGCGCCAGCGGGGATAAACC GTATTGACCAATTCATTCGGG ACAGTTATTAGTTCGAGTTCCC CGCGCCAGCGGGGATAAACCGT TACCATTCTGTTGCTTTTATGT ATAAGAATCGAGTTCCCCGCGC CAGCGGGGATAAACCG gltA2-FOR GCGCCAGCGGGGATAAACCGT 166 pCASCADE-udhA ATTGACCAATTCATTC pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 167 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 168 pCASCADE-FG1G2 GGGCAAG gltA1-REV CCTGTTTATCCCCGCTGGCGCG 169 GGGAACTCGAACTTCATAACT TTTAC TCGAGTTCCCCGCGCCAGCGGG 170 FG1G2Z GATAAACCGTTGATTATAATAA CCGTTTATCTGTTCGTATCGAG TTCCCCGCGCCAGCGGGGATAA ACCGAAAAGCATATAATGCGT AAAAGTTATGAAGTTCGAGTTC CCCGCGCCAGCGGGGATAAACC GTATTGACCAATTCATTCGGG ACAGTTATTAGTTCGAGTTCCC CGCGCCAGCGGGGATAAACCGC TCGTAAAAGCAGTACAGTGCA CCGTAAGATCGAGTTCCCCGCG CCAGCGGGGATAAACCG gltA2-FOR GCGCCAGCGGGGATAAACCGT 171 pCASCADE-zwf ATTGACCAATTCATTC pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 172 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 173 pCASCADE-FG1G2 GGGCAAG gltA1-REV CGGTTTATCCCCGCTGGCGCG 174 CTGGAACTCGAACTTCATAACT TTTAC G1G2UA TCGAGTTCCCCGCGCCAGCGGG 175 GATAAACCGAAAAGCATATAAT GCGTAAAAGTTATGAAGTTCG AGTTCCCCGCGCCAGCGGGGAT AAACCGTATTGACCAATTCATT CGGGACAGTTATTAGTTCGAGT TCCCCGCGCCAGCGGGGATAAA CCGTTACCATTCTGTTGCTTTT ATGTATAAGAATCGAGTTCCCC GCGCCAGCGGGGATAAACCGGT TTTTGTAATTT TACAGGCAAC CTTTATTCGAGTTCCCCGCGC CAGCGGGGATAAACCG gapAP1-FOR GCGCCAGCGGGGATAAACCGG 176 pCASCADE-gapAP1 TTTTTGTAATTT TACAGGC pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 177 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCACTGC 178 pCASCADE-G1G2U GGGCAAG udhA-REV CGGTTTATCCCCGCTGGCGCG 179 GGGAACTCGATTCTTATACAT AAAAGC G1G2UZ TCGAGTTCCCCGCGCCAGCGGG 180 GATAAACCGAAAAGCATATAAT GCGTAAAAGTTATGAAGTTCG AGTTCCCCGCGCCAGCGGGGAT AAACCGTATTGACCAATTCATT CGGGACAGTTATTAGTTCGAGT TCCCCGCGCCAGCGGGGATAAA CCGTTACCATTCTGTTGCTTTT ATGTATAAGAATCGAGTTCCCC GCGCCAGCGGGGATAAACCGCT CGTAAAAGCAGTACAGTGCAC CGTAAGATCGAGTTCCCCGCGC CAGCGGGGATAAACCG zwf-FOR GCGCCAGCGGGGATAAACCGC 181 pCASCADE-zwf TCGTAAAAG pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 182 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 183 pCASCADE-G1G2U GGGCAAG udhA-REV CGGTTTATCCCCGCTGGCGCG 184 GGGAACTCGATTCTTATACAT AAAAGC FG1G2UA TCGAGTTCCCCGCGCCAGCGGG 185 GATAAACCGTTGATTATAATAA CCGTTTATCTGTTCGTATCGAG TTCCCCGCGCCAGCGGGGATAA ACCGAAAAGCATATAATGCGT AAAAGTTATGAAGTTCGAGTTC CCCGCGCCAGCGGGGATAAACC GTATTGACCAATTCATTCGGG ACAGTTATTAGTTCGAGTTCCC CGCGCCAGCGGGGATAAACCGT TACCATTCTGTTGCTTTTATGT ATAAGAATCGAGTTCCCCGCGC CAGCGGGGATAAACCGGTTTTT GTAATTT TACAGGCAACCTTT TATTCGAGTTCCCCGCGCCAGC GGGGATAAACCG gapAP1-FOR GCGCCAGCGGGGATAAACCGG 186 pCASCADE-gapAP1 TTTTTGTATTT TACAGGC pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 187 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 188 pCASCADE- GGGCAAG FG1G2U udhA-REV CGGTTTATCCCCGCTGGCGCG 189 GGGAACTCGATTCTTATACAT AAAAGC FG1GUZ TCGAGTTCCCCGCGCCAGCGGG 190 GATAAACCGTTGATTATAATAA CCGTTTATCTGTTCGTATCGAG TTCCCCGCGCCAGCGGGGATAA ACCGAAAAGCATATAATGCGT AAAAGTTATGAAGTTCGAGTTC CCCGCGCCAGCGGGGATAAACC GTATTGACCAATTCATTCGGG ACAGTTATTAGTTCGAGTTCCC CGCGCCAGCGGGGATAAACCGT TACCATTCTGTTGCTTTTATGT ATAAGAATCGAGTTCCCCGCGC CAGCGGGGATAAACCGCTCGTA AAAGCAGTACAGTGCACCGTA AGATCGAGTTCCCCGCGCCAGC GGGGATAAACCG zwf-FOR GCGCCAGCGGGGATAAACCGC 191 pCASCADE-zwf TCGTAAAAG pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 192 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 193 pCASCADE- GGGCAAG FG1G2U udhA-REV CGGTTTATCCCCGCTGGCGCG 194 CTGGAACTCGATTCTTATACAT AAAAGC FG1G2UZA TCGAGTTCCCCGCGCCAGCGCTG 195 GATAAACCGTTGATTATAATAA CCGTTTATCTGTTCGTATCGAG TTCCCCGCGCCAGCGGGGATAA ACCGAAAAGCATATAATGCGT AAAAGTTATGAAGTTCGAGTTC CCCGCGCCAGCGGGGATAAACC GTATTGACCAATTCATTCGGG ACAGTTATTAGTTCGAGTTCCC CGCGCCAGCGGGGATAAACCGT TACCATTCTGTTGCTTTTATGT ATAAGAATCGAGTTCCCCGCGC CAGCGGGGATAAACCGCTCGTA AAAGCAGTACAGTGCACCGTA AGATCGAGTTCCCCGCGCCAGC GGGGATAAACCGGTTTTTGTAA TTT TACAGGCAACCTTTTATTC GAGTTCCCCGCGCCAGCGGGGA TAAACCG gapAP1-FOR GCGCCAGCGGGGATAAACCGG 196 pCASCADE-gapAP1 TTTTTGTAATTT TACAGGC pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 197 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 198 pCASCADE- GGGCAAG FG1G2UZ zwf-REV CGGTTTATCCCCGCTGGCGCG 199 GGGAACTCGATCTTACGGTGC ACTGTAC UZ TCGAGTTCCCCGCGCCAGCGGG 200 GATAAACCGTTACCATTCTGTT GCTTTTATGTATAAGAATCGAG TTCCCCGCCTCCAGCGGGGATAA ACCGCTCGTAAAAGCAGTACA GTGCACCGTAAGATCGAGTTCC CCGCGCCAGCGGGGATAAACCG zwf-FOR GCGCCAGCGGGGATAAACCGC 201 pCASCADE-zwf TCGTAAAAG pCASCADE-REV CTTGCCCGCCTGATGAATGCTC 202 ATCCGG pCASCADE-FOR CCGGATGAGCATTCATCAGGC 203 pCASCADE-udhA GGGCAAG -59- udhA-REV CGGTTTATCCCCGCTGGCGCG 204 GGGAACTCGATTCTTATACAT AAAAGC

TABLE 10 List of plasmids used in this study. Plasmid Utilized in this Study Plasmid Purpose Source pSIM5 Recombineering and Strain Construction Court Lab⁵⁴ pCP20 FRT kanamycin cassette curing Court Lab⁵⁴ pSMART-HC-Kan Backbone Vector Lucigen pcrRNA.Tet pCASCADE-control backbone Beisel Lab³⁴ Plasmid Constructed in this Study Plasmid Plasmid Name Addgene ID pSMART-Ala2 pSMART-HCKan-IN:yibDp-ald* 71326 pSMART-Ala3 pSMART-HCKan-IN:phoBp-ald* 71327 pSMART-Ala4 pSMART-HCKan-IN:phoHp-ald* 71328 pSMART-Ala5 pSMART-HCKan-IN:mipAp-ald* 71329 pSMART-Ala11 pSMART-HCKan-proA-ald* 87172 pSMART-Ala12 pSMART-HCKan-proC-ald* 87173 pSMART-Ala13 pSMART-HCKan-proD-ald* 87174 pSMART-Ala14 pSMART-HCKan-proB-ald* 101079 pSMART-Ala15 pSMART-HCKan-HCEp-ald* 101080 pSMART-Mev2 pSMART-IN:yibDp1-mvaE-IN:phoBp2-mvaS(A110G) 66642 pSMART-Mev3 pSMART-IN:yibDp1-mvaE-IN:mipAp2-mvaS(A110G) 102761 pSMART-Mev4 pSMART-IN:yibDp1-mvaE-IN:phoHp2-mvaS(A110G) 102762 pSMART-Mev5 pSMART-IN:mipAp1-mvaE-IN:yibD2-mvaS(A110G) 102763 pSMART-3HP pSMART-3HP-NADPH-rhtA 87143 pCDF-mcherry2 pCDF-proD-mcherry-DAS4 87145 pSMART-GFPuv pSMART-IN:yibDp-GFPuv 65822 pSMART-GFPuv2 pSMART-IN:phoBp-GFPuv 71517 pSMART-GFPuv3 pSMART-IN:phoUp-GFPuv 71518 pSMART-GFPuv4 pSMART-IN:phoHp-GFPuv 71519 pSMART-GFPuv5 pSMART-IN:mipAp-GFPuv 71520 pCASCADE-control pCASCADE 65821 pCASCADE-proD pCASCADE-proD 65820 pCASCADE-gapAP1 pCASCADE-gapAP1 87146 pCASCADE-fabI pCASCADE-fabI 66635 pCASCADE-FG1 pCASCADE-fabI-gltA1 71340 pCASCADE-FG1G2 pCASCADE-fabI-gltA1-gltA2 71342 pCASCADE-FG1G2A pCASCADE-fabI-gltA1-gltA2-gapA 87147 pCASCADE-FG1G2U pCASCADE-fabI-gltA1-gltA2-udhA 66637 pCASCADE-FG1G2UA pCASCADE-fabI-gltA1-gltA2-udhA-gapA 87154 pCASCADE-FG1G2UZ pCASCADE-fabI-gltA1-gltA2-udhA-zwf 87148 pCASCADE-FG1G2UZA pCASCADE-fabI-gltA1-gltA2-udhA-zwf-gapA 87149 pCASCADE-FG1G2Z pCASCADE-fabI-gltA1-gltA2-zwf 66638 pCASCADE-FG2 pCASCADE-fabI-gltA2 71341 pCASCADE-FU pCASCADE-fabI-udhA 66636 pCASCADE-FZ pCASCADE-fabI-zwf 71335 pCASCADE-G1G2 pCASCADE-gltA1-gltA2 71348 pCASCADE-G1G2A pCASCADE-gltA1-gltA2-gapA 87150 pCASCADE-G1G2U pCASCADE-gltA1-gltA2-udhA 71343 pCASCADE-G1G2UA pCASCADE-gltA1-gltA2-udhA-gapA 87151 pCASCADE-G1G2UZ pCASCADE-gltA1-gltA2-udhA-zwf 87152 pCASCADE-GG2Z pCASCADE-gltA1-gltA2-zwf 71347 pCASCADE-G1U pCASCADE-gltA1-udhA 71339 pCASCADE-G1Z pCASCADE-gltA1-zwf 71337 pCASCADE-G2U pCASCADE-gltA2-udhA 65819 pCASCADE-G2Z pCASCADE-gltA2-zwf 71338 pCASCADE-gltA1 pCASCADE-gltA1 71334 pCASCADE-gltA2 pCASCADE-gltA2 65817 pCASCADE-udhA pCASCADE-udhA 65818 pCASCADE-UZ pCASCADE-udhA-zwf 87153 pCASCADE-zwf pCASCADE-zwf 65825

Section 7: 2-Stage Micro-Fermentations

E. coli Media Stock Solutions

-   -   10× concentrated Ammonium-Citrate 30 salts (1 L), mix 30 g of         (NH₄)₂SO₄ and 1.5 g citric acid in water with stirring, adjust         pH to 7.5 with 10 M NaOH. Autoclave and store at room         temperature (RT).     -   10× concentrated Ammonium-Citrate 90 salts (1 L), mix 90 g of         (NH₄)₂SO₄ and 2.5 g citric acid in water with stirring, adjust         pH to 7.5 with 10 M NaOH. Autoclave and store at RT.     -   1 M Potassium 3-(N-morpholino) propanesulfonic Acid (MOPS),         adjust to pH 7.4 with 50% KOH. Filter sterilize (0.2 μm) and         store at RT.     -   0.5 M potassium phosphate buffer, pH 6.8, mix 248.5 mL of 1.0 M         K₂HPO₄ and 251.5 mL of 1.0 M KH₂PO₄ and adjust to a final volume         of 1000 mL with ultrapure water. Filter sterilize (0.2 μm) and         store at RT.     -   2 M MgSO₄ and 10 mM CaSO₄ solutions. Filter sterilize (0.2 μm)         and store at RT.     -   50 g/L solution of thiamine-HCl. Filter sterilize (0.2 μm) and         store at 4° C.     -   500 g/L solution of glucose, dissolve by stirring with heat.         Cool, filter sterilize (0.2 μm), and store at RT.     -   100 g/L yeast extract, autoclave, and store at RT.     -   100 g/L casamino acid, autoclave, and store at RT.     -   500× Trace Metal Stock: Prepare a solution of micronutrients in         1000 mL of water containing 10 mL of concentrated H₂SO₄, 0.6 g         CoSO₄.7H₂O, 5.0 g CuSO₄.5H₂O, 0.6 g ZnSO₄.7H₂O, 0.2 g         Na₂.MoO₄.2H₂O, 0.1 g H₃BO₃, and 0.3 g MnSO₄.H₂O. Filter         sterilize (0.2 μm) and store at RT in the dark.     -   Prepare a fresh solution of 40 mM ferric sulfate heptahydrate in         water, filter sterilize (0.2 μm) before preparing media each         time.

Media Components

Prepare the final working medium by aseptically mixing stock solutions based on the following tables in the order written to minimize precipitation, then filter sterilize (with a 0.2 μm filter).

TABLE 11 Seed Media, pH 6.8: Ingredient Unit SM10 SM10++ (NH₄)₂SO₄ g/L 9 9 Citric Acid g/L 0.25 0.25 Potassium mM 5 5 Phosphate CoSO₄•7H₂O g/L 0.0048 0.0048 CuSO₄•5H₂O g/L 0.04 0.04 ZnSO₄•7H₂O g/L 0.0048 0.0048 Na₂MoO₄•2H₂O g/L 0.0016 0.0016 H₃BO₃ g/L 0.0008 0.0008 MnSO₄•H₂O g/L 0.0024 0.0024 FeSO₄•7H₂O g/L 0.044 0.044 MgSO₄ mM 2.5 2.5 CaSO₄ mM 0.06 0.06 Glucose g/L 45 45 MOPS mM 200 200 Thiamine-HCl g/L 0.01 0.01 Yeast Extract g/L 1 2.5 Casamino Acids g/L 0 2.5

TABLE 12 Production/Wash Media, pH 6.8: FGM3 No FGM3 FGM3 + 40 mM Ingredient Unit FGM3 Phosphate Wash phosphate FGM10 (NH₄)₂SO₄ g/L 3 3 3 3 9 Citric Acid g/L 0.5 0.15 0.15 0.15 0.25 Potassium mM 1.8 0 0 40 5 Phosphate CoSO₄•7H₂O g/L 0.0024 0.0024 0 0.0024 0.0048 CuSO₄•5H₂O g/L 0.02 0.02 0.00 0.02 0.04 ZnSO₄•7H₂O g/L 0.0024 0.0024 0 0.0024 0.0048 Na₂MoO₄•2H₂O g/L 0.0008 0.0008 0 0.0008 0.0016 H₃BO₃ g/L 0.0004 0.0004 0 0.0004 0.0008 MnSO₄•H₂O g/L 0.0012 0.0012 0 0.0012 0.0024 FeSO₄•7H₂O g/L 0.022 0.022 0 0.022 0.044 MgSO₄ mM 2 2 0 2 2.5 CaSO₄ mM 0.05 0.05 0 0.05 0.06 Glucose g/L 45 25 0 45 25 MOPS mM 200 200 0 200 0 Thiamine-HCl g/L 0.01 0.01 0 0.01 0.01

Micro-Fermentations

An overview of the micro-fermentation protocol is illustrated in FIG. 16A-C. Strains were evaluated for production in 96 well plate micro-fermentations, wherein cells were initially grown to mid-log phase, harvested, washed, resuspended and normalized in a phosphate free production medium to an OD600=1, for a 24 hour production stage. The success of the micro-fermentations required: (1) syncing strains up by harvesting all strains in exponential phase; (2) the use of low biomass levels, so that batch sugar could be kept low while enabling significant potential product accumulation; and (3) a method to supply adequate mixing and aeration, while minimizing evaporative losses. To address the final requirement, commercially available microplate sandwich covers and clamps from EnzyScreen™ was used, which greatly reduce evaporative losses while enabling high levels of mixing and aeration in standard 25 mm orbit shakers operating at 400 rpm⁹²⁻⁹³. Micro-fermentation results for alanine production with different insulated phosphate promoters are shown in FIG. 17. Micro-fermentation results for strains evaluated with gapA and gapN gene alterations are given in FIG. 18.

Section 8: Micro-Fermentations Robustness Evaluation

During micro-fermentation oxygen robustness studies, production culture volume was varied to achieve desired oxygen transfer rate (OTR) values as previously reported (http://www.enzyscreen.com/oxygen_transfer_rates.htm)^(92,93), and as listed below in Table 14. Batch glucose levels during the production stage were altered to assess robustness to glucose. Strains utilized in the robustness experiments at the micro-fermentation scale are listed in Table 15. Results from the micro-fermentation robustness studies are given in FIGS. 19A-D, FIGS. 20A-D, FIGS. 21A-D, FIGS. 22A-D, FIGS. 23A-D, FIGS. 24A-D, FIGS. 25A-D, FIGS. 26A-D, FIGS. 27A-D, FIGS. 28A-D, FIGS. 29A-D, FIGS. 30A-D, FIGS. 31A-D, and FIG. 32.

TABLE 14 Culture conditions for different OTR values. 25 mm orbit shaker Max OTR Shaking Speed Fill Volume (mmol/L-hr) (rpm) (μL) 25 400 100 20 400 150 15 400 200

TABLE 15 List of strains used for micro-fermentation robustness evaluations and their RS scores. Strain # Silencing Proteolysis Plasmid RS 1 gltA1 FU pSMART-Ala2 89.6 2 gltA1 F pSMART-Ala2 89.5 3 gltA1 GU pSMART-Ala2 89.4 4 FG1G2 None pSMART-Ala2 89.3 5 G1G2 GU pSMART-Ala2 88.8 6 FG1G2 G pSMART-Ala2 88.2 7 G1G2 F pSMART-Ala2 83.4 8 gltA2 FGU pSMART-Ala2 83.4 9 gltA1 FGU pSMART-Ala2 83.1 10 G1G2 FGU pSMART-Ala2 82.3 11 gltA2 U pSMART-Ala2 82.2 12 gltA2 F pSMART-Ala2 80.6 13 FG1G2 FG pSMART-Ala2 80.5 14 None G pSMART-Ala2 79.9 15 gltA2 GU pSMART-Ala2 77.9 16 fabI FGU pSMART-Ala2 75.7 17 None FG PSMART-Ala2 75.4 18 G1G2 FU pSMART-Ala2 75.3 19 None FGU pSMART-Ala2 73.4 20 None FU pSMART-Ala2 73.3 21 gltA1 U pSMART-Ala2 72.9 22 fabI FG pSMART-Ala2 69.1 23 FG1G2 FU pSMART-Ala2 67.6 24 gltA2 FU pSMART-Ala2 67.5 25 None F pSMART-Ala2 65.6 26 gltA2 FG pSMART-Ala2 62.1 27 FG1G2 F pSMART-Ala2 61.1 28 fabI GU pSMART-Ala2 59.9 29 fabI F pSMART-Ala2 59.6 30 gltA1 FG pSMART-Ala2 58.1 31 gltA1 None pSMART-Ala2 57.1 32 None None pSMART-Ala2 55.5 33 G1G2 None pSMART-Ala2 54.1 34 fabI U pSMART-Ala2 53.9 35 gltA2 G pSMART-Ala2 52.8 36 fabI None pSMART-Ala2 50.3 37 fabI FU pSMART-Ala2 48.4 38 gltA2 None pSMART-Ala2 47.8 39 FG1G2 FGU pSMART-Ala2 44.6 40 None GU pSMART-Ala2 42.9 41 None U pSMART-Ala2 39.3 42 fabI G pSMART-Ala2 39.2 43 gltA1 G pSMART-Ala2 34.7 44 G1G2 FG pSMART-Ala2 32.8 45 FG1G2 U pSMART-Ala2 29.4 46 FG1G2 GU pSMART-Ala2 24.3 47 G1G2 G pSMART-Ala2 24.1 48 G1G2 U pSMART-Ala2 −25.3 49 None None pSMART-Ala13 55.7 50 None None pSMART-Ala12 −31.5 51 None None pSMART-Ala15 −103.2 52 None None pSMART-Ala11 −114.1 53 None None pSMART-Ala14 −441.5

Section 9: Standardized 2-Stage Fermentations

A standardized phosphate limited 2-stage fermentation protocol was utilized for evaluation of all valve strains. This protocol yields highly reproducible growth stage results, with minimal strain to strain variability even with strains making different products. More significant variability was observed during the production stage as a result of differing feed rates and base utilization by different strains. FIG. 33A gives the growth curves for all valve strains with a 10 g·cdw/L biomass level in 1 L fermentations performed in this study. This consistency is contrasted to the more variable growth of growth associated production strains, given in FIG. 33B.

TABLE 16 Strains used for mevalonic acid scalability. Strain # Silencing Proteolysis Plasmid 1 FG1G2 FU pSMART-Mev2 2 G2Z FGUA pSMART-Mev2 3 FG1G2A FUN pSMART-Mev2 4 UZ FGUA pSMART-Mev2

Section 10: Analytical Methods

TABLE 17 UPLC-MS/MS parameters Retention ESI MRM Cone Collision Analyte Time (min) Mode Transition(s) Voltage Energy Alanine 0.5 + 89.95→44.08 15 9 C13-Alanine 0.5 + 91.95→46.08 15 9

DETAILED DESCRIPTION OF FIGURES

FIG. 1A: An Overview of Dynamic Metabolic Control in 2-Stage Fermentations. Metabolic engineering involves optimizing a metabolic pathway to a desired product to the existing metabolic network of a host, converting feedstocks to a desired product. Filled circles indicate metabolites and lines indicate enzymatic reactions. Traditional optimization in metabolic engineering, often involves three key steps (a) the deletion of competing non-essential metabolic pathways including those leading to undesired byproducts and the overexpression of enzymes in the pathway converting feedstock molecules to the product (indicated by thicker lines) and potentially (b) attenuating enzymes in essential metabolism (indicated by orange lines) to further increase production. This process is iterated to optimize the yield to the desired product (pie charts). By contrast, dynamic metabolic network minimization can be used to fully unlock the potential of commonly used 2-stage fermentation processes (c-d). In the first stage of these processes (c) biomass growth and yield are optimized, while in the second stage (d) product formation is optimized, which is well suited for a 2-stage process (e) in which biomass levels accumulate and consume a limiting nutrient (in this case inorganic phosphate), which when depleted triggers entry into a productive stationary phase. Synthetic metabolic valves utilizing CRISPRi based gene silencing and/or controlled proteolysis can be used (f and g) to greatly reduce the pertinent metabolic network upon the transition to the production stage, (f) and array of silencing guides can be induced, processed by the CASCADE complex into individual guides and used to silencing target multiple genes of interest (GOI). (g) If C-terminal DAD+4 lags are added to enzymes of interest (EOI) through chromosomal modification, they can be inducibly degraded by the clpXP protease in the present of and inducible sspB chaperone. (h) Dynamic control over protein levels in E. coli using 2 stage dynamic control with inducible proteolysis and CRISPRi silencing. As cells grow phosphate is depleted, and cells “turn off mCherry and “turn on” GFPuv. Shaded areas represent one standard deviation from the mean, n=3. (i) Relative impact of proteolysis and gene silencing alone and in combination on mCherry degradation, with (j) decays rates.

FIG. 1B: Strain and Bioprocess Optimization. (a) Conventional approaches for strain and process optimization in metabolic engineering often involves deletion of competing non-essential metabolic pathways and overexpression of pathway enzymes (Filled circles: metabolites; lines: enzymatic reactions, green indicated a production pathway). (a-i) Strain variants are evaluated at screening scale (microtiter plates, shake flasks, etc), (a-ii) the best strains are assessed in larger scale instrumented bioreactors. Numerous design-build-test cycles (a-vi-vii) are used to iteratively optimize both the production strain and process, including the often-critical optimization of environmental (process) variables (a-vii). (a-iii) The best performing strains and associated optimized process conditions are scaled to industrially relevant levels. (b) Rapid strain and bioprocess optimization using 2-stage dynamic metabolic control. The metabolic network in the cell is dynamically minimized to only the steps essential for product formation. This is accomplished in a standardized 2-stage bioprocess (c), where a biomass accumulating growth stage is followed by a production stage, with only a minimal metabolic network. The limitation of a macronutrient can be used to “switch” cellular metabolism from growth to production. The approach results in a smaller subset of potential strain variants for screening (b-i). Metabolic network minimization helps increase relevant metabolite levels (d) and thus production levels, it also enhances process robustness (e), and as a result process and strain scalability (f). The best producers identified from screening are predictably and rapidly scaled to (b-ii) larger instrumented bioreactors, and (b-iii) subsequently to industrially relevant levels. If needed, limited design-build-test cycles (b-iv) are incorporated to guide improvements. Product independent, standardized protocols are followed for strain evaluation at all scales, eliminating the need for intensive process optimization.

FIGS. 2A-D: Implementation of 2-stage Synthetic Metabolic Valves (SMVs) in E. coli. FIG. 2A depicts SMVs utilizing CRISPRi based gene silencing and/or controlled proteolysis were constructed. (Top) Silencing: An array of inducible silencing guide RNAs (i) can be used to silence expression of multiple genes of interest (GOI) when the native E. coli CRISPR/Cascade machinery is expressed, which can process guide arrays into individual guides (ii). (Bottom) Proteolysis: When C-terminal DAS+4 tags are added to enzymes of interest (EOI) (through chromosomal modification), they can be degraded by the clpXP protease (iv) upon the controlled induction of the sspB chaperone (iii). FIG. 2B depicts dynamic control over protein levels in E. coli using inducible proteolysis and CRISPRi silencing. As cells grow phosphate is depleted, cells “turn OFF” mCherry and “turn ON” GFPuv. Shaded areas represent one standard deviation from the mean, r.f.u, relative fluorescence units. FIG. 2C depicts relative impact of proteolysis and gene silencing alone and in combination on mCherry degradation, n.f.u. normalized fluorescence units (normalized to maximal fluorescence). FIG. 2D depicts relative impact of proteolysis and gene silencing alone and in combination on observed mCherry fluorescence decays rates (per hour).

FIGS. 3A-K: Alanine Production in E. coli utilizing 2-stage Dynamic Control. FIG. 3A depicts strain variant design. Primary pathways in central metabolism are shown including: Glycolysis, the Pentose Phosphate Pathway, the Citric Acid Cycle (TCA), Fatty Acid Biosynthesis, and the Soluble Transhydrogenase. Key valve candidate enzymes/genes that are “turned OFF” to reduce flux through central metabolism can include: glucose-6-phosphate dehydrogenase (zwf- “Z”), lipoamide dehydrogenase (lpd- “L”), citrate synthase (gltA- “G”), enoyl-ACP reductase (fabI-“F”), and the soluble transhydrogenase (udhA-“U”). Importantly, dynamic elimination of fabI has been previously demonstrated to increase intracellular malonyl-CoA pools as well as malonyl-CoA flux⁵⁵. Enzymes that are dynamically “turned ON” can include the metabolic pathways to produce the products of interest, in this case alanine. Specific pathway enzymes include an NADPH-dependent alanine dehydrogenase (ald*) and an alanine exporter (alaE). Additionally, as the alanine production pathway utilizes NADPH as a cofactor, the NADPH-dependent glyceraldehyde-3-phosphate dehydrogenase encoded by the gapN gene⁵⁶ from S. mutans was turned on alone and in combination with turning off the native gapA- “A” gene (NADH dependent glyceraldehyde dehydrogenase). Abbreviation: PTS—glucose phosphotransferase transport system, P—phosphate, BP—bisphosphate, OAA—oxaloacetate, DHAP—dihydroxyacetone phosphate, GA3P—glyceraldehyde-3-phosphate, 1,3-BPG—1,3 bisphosphoglycerate, 3-PG—3-phosphoglycerate, 2-PG—2-phosphoglycerate, PEP—phosphoenolpyruvate, MSA—malonate semialdehyde, ACP—acyl carrier protein, Ru—ribulose, Xu—xylulose, E—erthryose, Ri—ribose, S—sedoheptulose. Strains were engineered with SMVs for the dynamic control of all combinations of valve genes/enzymes, either through gene silencing alone, proteolysis alone, or the combination of both. These strains were evaluated for alanine production in standardized micro-fermentations. FIG. 3B depicts rank order plot for average alanine titer (black) of all valve strains examined in 2-stage micro-fermentation, grey area represents standard deviation. Alanine production in the control strain was colored in red. FIG. 3C depicts average alanine titer in 2-stage production in response to different proteolysis and silencing combinations, from 0 g/L (purple) to 5 g/L (red). FIG. 3D depicts average alanine titer in response to different oxygen transfer rates (OTR) and glucose concentrations evaluated for a single “Valve” alanine strain (Silencing of gltA1 (“G1”), Proteolysis of fabI and udhA (“FU”)). The results of this surface were used to calculate a strain-specific robustness score (RS) (refer to text), this strain has the highest RS score. FIG. 3E depicts a heat map of the robustness score for a subset of 48 “Valve” strains evaluated across multiple process conditions. FIG. 3F depicts scale up of one of the best producing strain from micro-fermentations (Silencing of fabI-gltA1-gltA2 (“FG1G2”), Proteolysis of fabI, gltA and udhA (“FGU”)) to 1 L bioreactors results in a titer of 80 g/L after 48 hrs of production, with a yield of 0.8 g/g. FIG. 3G depicts overexpression of the alaE alanine exporter in this strain (Panel f) results in significantly improved production, reaching 147 g/L in 27 hrs of production, with a yield of ˜1 g/g. (Refer to Supplemental Materials, Section 3 for additional details). FIG. 3H depicts strains selected for robustness evaluation in micro-fermentations. FIG. 3I depicts robustness and titer for the most robust “Valve” alanine strain (Silencing_gltA1, Proteolysis_FU). Bottom surface shows heat map for the alanine titer normalized to the median of all process conditions assessed, upper surface shows alanine tiler under all process conditions, the same color scale (alanine titer in g/L) was used for both panels. FIG. 3J depicts RS3 scores for the selected strains. FIG. 3K depicts process reproducibility heat map for all conditions evaluated, the same grayscale was used for FIG. 3J and FIG. 3K.

FIGS. 4A-F: Robustness Comparison Between 2-Stage and Growth Associated Approaches. FIG. 4A depicts rank order of the RS3 scores for all alanine strains evaluated, red bars indicate valve alanine strains, and blue bars indicate growth associated (GA) alanine strains. FIG. 4B depicts average RS3 score for “Valve” alanine strains with proteolysis “F” valve, and growth associated alanine strains. FIG. 4C depicts max titer plot for a representative “Valve” alanine (Proteolysis_FGU, Silencing_gtA 1), and growth associated alanine strains in micro-fermentation of all conditions evaluated. FIG. 4D depicts process reproducibility for growth associated alanine strains under all conditions evaluated. FIG. 4E depicts robustness and titer for a representative robust “Valve” alanine (Proteolysis_FGU, Silencing_gltA1) FIG. 4F depicts robustness and titer for the GA2 strain Bottom surface, heat map for the alanine titer normalized to the median of all process conditions assessed, upper surface, alanine titer under all process conditions, the same color scale (alanine titer in g/L) was used for both panels.

FIGS. 5A-J: Comparisons of “Valve” and growth associated alanine production in micro-fermentations (FIGS. 5A-D) and 1 L fermentation (FIGS. 5E-J). Average alanine titer (FIG. 5A) and robustness score (FIG. 5B) for all strains used for robustness analysis. Average alanine titer in response to different OTR and glucose concentrations for selected “Valve” (FIG. 5C) and growth associated (FIG. 5D) alanine strains. Strains marked by asterisk in (FIG. 5B) were used for this analysis. These two strains were selected for 1 L performance comparison. FIG. 5E and FIG. 5F depicts 1 L performance metrics evaluated, including average specific productivity (SP, g/gdcw-h), average glucose uptake rate (GUR, g/gcdw-h), max titer (g/L), and max yield (g/g). FIG. 5G and FIG. 5H depicts μL to 1 L scalability. 1 L data was standardized to the maximal titer within 50 hours of production. Adequate feed was used for growth associated strains to avoid glucose depletion. FIG. 5I and FIG. 5J depicts 1 L production profiles for all strains used in scalability plot FIG. 5G and FIG. 5H respectively, darker symbols represent growth curves, lighter symbols represent production curves, shape of symbols encode the same strains in FIG. 5G or FIG. 5H.

FIG. 6A-E: Mevalonate Production in E. coli utilizing 2-stage Dynamic Control. FIG. 6A depicts Metabolic Pathways and SMVs for mevalonate production. FIG. 6B depicts mevalonate production using several production pathway plasmid variants with varied promoter combinations in the control strain. FIG. 6C depicts micro-fermentation results for a subset of “Valve” strains producing mevalonate, using the best production pathway from FIG. 6B, along with combinations of proteolytic and silencing SMVs. FIG. 6D depicts μL to 1 L scalability for a subset of mevalonate strains evaluated at the 1 L scale. n=3 for μL data and n=1 for 1 L data. The maximal titer within 50 hours of production time was used for the correlation. FIG. 6E depicts production of the best mevalonate strain from FIG. 6D (Silencing of fabI-gltA1-gltA2 (“FG1G2”), Proteolysis of fabI and udhA (“FU”)) in 1 L bioreactors. A titer of 97 g/L was observed in 78 hrs of production. Yields during the production stage reached 0.46 g/g (84% of theoretical yield). (Refer to Supplemental Materials, Section 9 for additional details). FIG. 6F depicts micro-fermentation results for a subset of strains producing 3-HP. FIG. 6G depicts μL to 1 L scalability for a subset of 3-HP strains evaluated at the 1 L scale (Supplemental Materials Tables S21 and S22). FIG. 6H depicts production performance for the best 3-HP strains in the 1 L systems, squares, 3-HP/mevalonic acid titer, circles, OD600. Yields during the production stage reached for the 0.46 g/g for mevalonic acid and 0.63 g/g for 3-HP in the highest producers.

FIG. 7: Phosphate depletion promoter characterization. A set of GFP reporter vectors were constructed to assess the expression level of 12 previously identified phosphate regulated promoters. Strains were evaluated continuously for GFP expression in the Biolector™ using a standardized protocol wherein in minimal medium limited for phosphate is used. After Biomass levels reach a peak (not shown for clarity), GFP expression begins. Importantly the current set of promoters enables a large range of expression levels.

FIG. 8: Insulated phosphate depletion promoter characterization. A set of GFP reporter vectors were constructed to assess the expression level of five insulated phosphate regulated promoters in FGM3 media. Strains were evaluated continuously for GFP expression in the Biolector™ using a standardized protocol wherein in minimal medium limited for phosphate is used. After Biomass levels reach a peak (not shown for clarity), GFP expression begins. Importantly the current set of promoters enables a large range of expression levels.

FIG. 9: Insulated constitutive promoter characterization. A set of GFP reporter vectors were constructed to assess the expression level of five insulated constitutive promoters in FGM3 with 40 mM phosphate media. Shaded area represents standard deviations, n=3. Strains were evaluated continuously for GFP expression in the Biolector™. GFP expression was observed only for promoters proA, proB and proD.

FIG. 10: Metabolic modeling results for optimal 3-HP flux in two stage fermentations. LEFT: Optimized fluxes during the growth stage where biomass production was used as the objective function. RIGHT: Optimized fluxes during the 3-HP production stage where 3-HP production was used as the objective function (biomass production was set to 0). Fluxes are listed as relative ratios or moles of flux through a given reaction per 100 moles of glucose utilized.

FIG. 11: Chromosomal modifications.

FIG. 12: Average maximal growth rates of starting host strains in 1 L FGM10 minimal medium fermentations, n=2.

FIG. 13A-E: Distribution of glucose utilized during the growth phase of starting host strains in 1 L standard minimal medium fermentations. Mid exponential and final growth period results are given for DLF_0025 as “production” begins in mid-late exponential phase. Results are averages of duplicate fermentations. FIG. 13A, BW25113: FIG. 13B, BWapldf; FIG. 13C, DLF_0001; FIG. 13D, DLF_0025 at mid-exponential; FIG. 13E, DLF_0025 at end of growth phase. Unit was gram glucose.

FIG. 14: pCASCADE-control plasmid construction scheme.

FIG. 15A-B: pCASCADE construction scheme. FIG. 15A, single sgRNA cloning; FIG. 15B, double sgRNA.

FIG. 16A-C: Micro-fermentation process overview. (A) An overview of the high throughput micro-fermentation protocol. Freezer stocks (alternatively colonies may be used) are used to inoculate into SM10++ in 96 well plates. Cultures are grown overnight for 16 hours, harvested by centrifugation, washed with no-phosphate medium and resuspended in no-phosphate medium at target biomass levels. (OD600 nm=1.0). EnzyScreen™ covers and clamps are used to reduce evaporation and enable high oxygen transfer rates. The protocol is implemented with a Tecan Evo liquid handler. (B) Representative overnight growth in a 96 well plates culture, distribution of OD600 for overnight culture was plotted. (C) Representative OD600 distribution after normalization using Tecan Evo liquid handler.

FIG. 17: Micro-fermentation for L-alanine production using different insulated phosphate promoters in DLF_0025 strain.

FIG. 18: Heatmap for L-alanine production by gapN/gapA strains.

FIGS. 19A-D: Alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 20A-D: Alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 21A-D: Alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 22A-D: Alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 23A-D: Alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 24A-D: Alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 25A-D: Alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 26A-D: Alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 27A-D: Alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 28A-D: Alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 29A-D: Alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 30A-D: Alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIGS. 31A-D: Alanine production in response to different OTR and glucose concentration in micro-fermentation for 4 strains evaluated for robustness.

FIG. 32: Alanine production in response to different OTR and glucose concentration in micro-fermentation for one strain evaluated for robustness.

FIGS. 33A-B: Growth profile for all (FIG. 33A) valve and (FIG. 33B) growth associated strains at 1 L scale evaluated in this paper. Growth curves were synced to account for any variations in lag time. Valve strains growth curves were synced to the same mid-exponential point. Growth associated strains growth curves were synced to the same take-off point.

FIG. 34: Specific Productivity (SP) comparison for strain with highest mevalonate titer from literature and mevalonate strain 1 evaluated in this work.

FIG. 35: Alanine standard curve from MS measurement. Average and standard deviation for mass spec response from triplicate standard measurement were plotted.

FIGS. 36A-B: Glucose (FIG. 36A) and ethanol (FIG. 36B) standard curves from RI measurement. Average and standard deviation for peak area from triplicate standard measurement were plotted.

FIG. 37: 3-Hydroxypropionic acid standard curve from TUV measurement. Average and standard deviation for peak area from duplicate standard measurement were plotted.

FIGS. 38A-D: TUV standard curves for (FIG. 38A) L-alanine, (FIG. 38B) D-alanine, (FIG. 38C) mevalonic acid, and (FIG. 38D) mevalonolactone. Average and standard deviation for peak area from triplicate standard measurement were plotted.

REFERENCES

-   1. Cameron, D. E.; Bashor, C. J.; Collins, J. J., A brief history of     synthetic biology. Nat Rev Microbiol 2014, 12 (5), 381-90. -   2. Cheong, S.; Clomburg, J. M.; Gonzalez, R., Energy- and     carbon-efficient synthesis of functionalized small molecules in     bacteria using non-decarboxylative Claisen condensation reactions.     Nature biotechnology 2016, 34 (5), 556-61 -   3. Choi, S. Y.; Park, S. J.; Kim, W. J.; Yang, J. E.; Lee, H.; Shin,     J.; Lee, S. Y., One-step fermentative production of     poly(lactate-co-glycolate) from carbohydrates in Escherichia coli.     Nature biotechnology 2016, 34 (4), 435-40. -   4. Jarboe, L. R.; Zhang, X.; Wang, X.; Moore, J. C.; Shanmugam, K.     T.; Ingram, L. O., Metabolic engineering for production of     biorenewable fuels and chemicals: contributions of synthetic     biology. Journal of biomedicine & biotechnology 2010, 761042. -   5. Lee, J. W.; Na, D.; Park, J. M.; Lee, J.; Choi, S.; Lee, S. Y.,     Systems metabolic engineering of microorganisms for natural and     non-natural chemicals. Nat Chem Biol 2012, 8 (6), 536-46. -   6. Dellomonaco, C.; Clomburg, J. M.; Miller, E. N.; Gonzalez, R.,     Engineered reversal of the beta-oxidation cycle for the synthesis of     fuels and chemicals. Nature 2011, 476 (7360), 355-9. -   7. Kim, S.; Clomburg, J. M.; Gonzalez, R., Synthesis of medium-chain     length (C6-C10) fuels and chemicals via beta-oxidation reversal in     Escherichia coli. J Ind Microbiol Biotechnol 2015, 42 (3), 465-75. -   8. Meadows, A. L.; Hawkins, K. M.; Tsegaye, Y.; Antipov, E.; Kim,     Y.; Raetz, L.; Dahl, R. H.; Tai, A.; Mahatdejkul-Meadows, T.; Xu,     L.; Zhao, L.; Dasika, M. S.; Murarka, A.; Lenihan, J.; Eng, D.;     Leng, J. S.; Liu, C. L.; Wenger, J. W.; Jiang, H.; Chao, L.;     Westfall, P.; Lai, J.; Ganesan, S.; Jackson, P.; Mans, R.; Platt,     D.; Reeves, C. D.; Saija, P. R.; Wichmann, G.; Holmes, V. F.;     Benjamin, K.; Hill, P. W.; Gardner, T. S.; Tsong, A. E., Rewriting     yeast central carbon metabolism for industrial isoprenoid     production. Nature 2016, 537 (7622), 694-697. -   9. Yadav, V. G.; De Mey, M.; Lim, C. G.; Ajikumar, P. K.;     Stephanopoulos, G., The future of metabolic engineering and     synthetic biology: towards a systematic practice. Metab Eng 2012, 14     (3), 233-41 -   10. Brophy, J. A.; Voigt, C. A., Principles of genetic circuit     design. Nat Methods 2014, 11 (5), 508-20. -   11. Koutinas, M.; Kiparissides, A.; Pistikopoulos, E. N.;     Mantalaris, A., Bioprocess systems engineering: transferring     traditional process engineering principles to industrial     biotechnology. Comput Struct Biotechol J 2012, 3, e201210022. -   12. Rodrigo, G.; Jaramillo, A., AutoBioCAD: full biodesign     automation of genetic circuits. ACS Synth Biol 2013, 2 (5), 230-6. -   13. Garst, A. D.; Bassalo, M. C.; Pines, G.; Lynch, S. A.;     Halweg-Edwards, A. L.; Liu, R.; Liang, L.; Wang, Z.; Zeitoun, R.;     Alexander, W. G.; Gill, R. T., Genome-wide mapping of mutations at     single-nucleotide resolution for protein, metabolic and genome     engineering. Nat Biotech 2017, 35 (1), 48-55. -   14. Church, G. M.; Elowitz, M. B.; Smolke, C. D.; Voigt, C. A.;     Weiss, R., Realizing the potential of synthetic biology. Nat Rev Mol     Cell Biol 2014, 15 (4), 289-94. -   15. Thomas, S.; Maynard, N. D.; Gill, J., DNA library construction     using Gibson Assembly[reg]. Nat Meth 2015, 12 (11). -   16. Goodwin, S.; McPherson, J. D.; McCombie, W. R., Coming of age:     ten years of next-generation sequencing technologies. Nat Rev Genet     2016, 17 (6), 333-51. -   17. Lynch, M. D.; Warnecke, T.; Gill, R. T., SCALEs: multiscale     analysis of library enrichment. Nat Methods 2007, 4 (1), 87-93. -   18. Zeitoun, R. 1.; Garst, A. D.; Degen, G. D.; Pines, G.;     Mansell, T. J.; Glebes, T. Y.; Boyle, N. R.; Gill, R. T.,     Multiplexed tracking of combinatorial genomic mutations in     engineered cell populations. Nat Biotechnol 2015, 33 (6), 631-7. -   19. Crook, N.; Abatemarco, J.; Sun, J.; Wagner, J. M.; Schmitz, A.;     Alper, H. S., In vivo continuous evolution of genes and pathways in     yeast. Nat Common 2016, 7, 13051. -   20. Burg, J. M., Reed, B J., Ye, Z., Cooper, C. B., Moreb, E. A.,     and Lynch, M. D, Large-Scale Bioprocess Competitiveness: The     Potential of Dynamic Metabolic Control in Two-Stage Fermentations.     Current Opinions in Chemical Engineering 2016, (In Review). -   21. Zhang, Y. H., Production of biofuels and biochemicals by in     vitro synthetic biosystems: Opportunities and challenges. Biotechnol     Adv 2015, 33 (7), 1467-83. -   22. Dietrich, J. A., McKee, A. E.; Keasling, J. D., High-throughput     metabolic engineering: advances in small-molecule screening and     selection. Annu Rev Biochem 2010, 79, 563-90. -   23. Formenti, L. R.; Norregaard, A.; Bolic. A.; Hernandez, D. Q.;     Hagemann, T.; Heins, A. L.; Larsson, H.; Mears, L.;     Mauricio-Iglesias, M.; Kruhne, U.; Gernaey, K. V., Challenges in     industrial fermentation technology research. Biotechnol J 2014, 9     (6), 727-38. -   24. Levanon, S. S.; San, K. Y.; Bennett, G. N., Effect of oxygen on     the Escherichia coli ArcA and FNR regulation systems and metabolic     responses. Biotechnol Bioeng 2005, 89 (5), 556-64. -   25. Logue, J. B.; Findlay, S. E.; Comte, J., Editorial Microbial     Responses to Environmental Changes. Front Microbial 2015, 6, 1364. -   26. Garcia-Ochoa, F.; Gomez, E., Bioreactor scale-up and oxygen     transfer rate in microbial processes: an overview. Biotechnol Adv     2009, 27 (2), 153-76. -   27. Waegeman, H.; Beauprez, J.; Moens, H.; Maertens, J.; De Mey, M.;     Foulquie-Moreno, M. R.; Heijnen, J. J.; Charlier, D.; Soetaert, W.,     Effect of iclR and arcA knockouts on biomass formation and metabolic     fluxes in Escherichia coli K12 and its implications on understanding     the metabolism of Escherichia coli BL21 (DE3). BMC Microbiol 2011,     11, 70. -   28. Waegeman, H.; Maertens, J.; Beauprez, J.; De Mey, M.; Soetaert,     W., Effect of iclR and arcA deletions on physiology and metabolic     fluxes in Escherichia coli BL21 (DE3). Biotechnol Lett 2012, 34 (2),     329-37. -   29. Hemmerich, J., Adelantado, N.; Barrigon, J. M., Ponte, X.;     Hormann, A.; Ferrer, P.; Kensy, F.; Valero, F., Comprehensive clone     screening and evaluation of fed-batch strategies in a     microbioreactor and lab scale stirred tank bioreactor system:     application on Pichia pastoris producing Rhizopus oryzae lipase.     Microb Cell Fact 2014, 13 (1), 36. -   30. Ramirez-Vargas, R.; Vital-Jacome, M., Camacho-Perez, E.;     Hubbard, L.; Thalasso, F., Characterization of oxygen transfer in a     24-well microbioreactor system and potential respirometric     applications. J Biotechnol 2014, 186, 58-65. -   31. Huber, R.; Roth, S.; Rahmen, N.; Buchs, J., Utilizing     high-throughput experimentation to enhance specific productivity of     an E. coli T7 expression system by phosphate limitation BMC     biotechnology 2011, 11, 22. -   32. Lynch, M. D., Into new territory: improved microbial synthesis     through engineering of the essential metabolic network. Curr Opin     Biotechnol 2016, 38, 106-11. -   33. McGinness, K. E.; Baker, T. A.; Sauer, R. T., Engineering     controllable protein degradation. Mol Cell 2006, 22 (5), 701-7. -   34. Luo, M. L.; Mullis, A. S.; Leenay, R. T.; Beisel, C. L.,     Repurposing endogenous type I CRISPR-Cas systems for programmable     gene repression. Nucleic acids research 2015, 43 (1), 674-81. -   35. Qi, L. S.; Larson, M. H.; Gilbert, L. A.; Doudna, J. A.;     Weissman, J. S.; Arkin, A. P.; Lim, W. A., Repurposing CRISPR as an     RNA-guided platform for sequence-specific control of gene     expression. Cell 2013, 152 (5), 1173-83. -   36. Chubukov, V.; Sauer, U., Environmental dependence of     stationary-phase metabolism in Bacillus subtilis and Escherichia     coli. Applied and environmental microbiology 2014, 80 (9), 2901-9. -   37. Santos-Beneit, F., The Pho regulon; a huge regulatory network in     bacteria. Front Microbiol 2015, 6, 402. -   38. Brouns, S. J.; Jore, M. M.; Lundgren, M.; Westra, E. R.;     Slijkhuis, R. J.; Snijders, A. P.; Dickman, M. J.; Makarova, K. S.;     Koonin, E. V.; van der Oost, J., Small CRISPR RNAs guide antiviral     defense in prokaryotes. Science 2008, 321 (5891), 960-4. -   39. Jian, J.; Zhang, S. Q.; Shi, Z. Y.; Wang, W.; Chen, G. Q.; Wu,     Q., Production of polyhydroxyalkanoates by Escherichia coli mutants     with defected mixed acid fermentation pathways. Appl Microbiol     Biotechnol 2010, 87 (6), 2247-56. -   40. Grunenfelder, B.; Rummel, G.; Vohradsky, J.; Roder, D.; Langen,     H.; Jenal, U., Proteomic analysis of the bacterial cell cycle. Proc     Natl Acad Sci USA 2001, 98 (8), 4681-6. -   41. Hintsche, M.; Klumpp, S., Dilution and the theoretical     description of growth-rate dependent gene expression. J Biol Eng     2013, 7 (1), 22. -   42. Lerchner, A.; Jarasch, A.; Skerra, A., Engineering of alanine     dehydrogenase from Bacillus subtilis for novel cofactor specificity.     Biotechnol Appl Biochem 2016, 63 (5), 616-624. -   43. Hori, H.; Yoneyama, H.; Tobe, R.; Ando, T.; Isogai, E.;     Katsumata, R., Inducible L-alanine exporter encoded by the novel     gene ygaW (alaE) in Escherichia coli. Applied and environmental     microbiology 2011, 77 (12), 4027-34. -   44. Davis, J. H.; Rubin, A. J.; Sauer, R. T., Design, construction     and characterization of a set of insulated bacterial promoters.     Nucleic acids research 2011, 39 (3), 1131-41. -   45. Hedl, M.; Sutherlin, A.; Wilding, E. I.; Mazzulla, M.; McDevitt,     D.; Lane, P.; Burgner, J. W., 2nd; Lehnbeuter, K. R.;     Stauffacher, C. V.; Gwynn, M. N.; Rodwell, V. W., Enterococcus     faecalis acetoacetyl-coenzyme A     thiolase/3-hydroxy-3-methylglutaryl-coenzyme A reductase, a     dual-function protein of isopentenyl diphosphate biosynthesis. J     Bacteriol 2002, 184 (8), 2116-22. -   46. Steussy, C. N.; Robison, A. D.; Tetrick, A. M.; Knight, J. T.;     Rodwell, V. W.; Stauffacher, C. V.; Sutherlin, A. L., A structural     limitation on enzyme activity: the case of HMG-CoA synthase.     Biochemistry 2006, 45 (48), 14407-14. -   47. Xiong, M.; Schneiderman, D. K.; Bates, F. S.; Hillmyer, M. A.,     Zhang, K., Scalable production of mechanically tunable block     polymers from sugar. Proc Natl Acad Sci USA 2014, 111 (23), 8357-62. -   48. Otterstedt, K.; Larsson, C.; Bill, R. M.; Stahlberg, A.; Boles,     E.; Hohmann, S.; Gustafsson, L., Switching the mode of metabolism in     the yeast Saccharomyces cerevisiae. EMBO Rep 2004, 5 (5), 532-7. -   49. Hubmann, G.; Guillouet, S.; Nevoigt, E., Gpd1 and Gpd2     fine-tuning for sustainable reduction of glycerol formation in     Saccharomyces cerevisiae. Applied and environmental microbiology     2011, 77 (17), 5857-67. -   50. Lascaris, R; Bussemaker, H. J.; Boorsma, A.; Piper, M.; van der     Spek, H.; Grivell, L.; Blom, J., Hap4p overexpression in     glucose-grown Saccharomyces cerevisiae induces cells to enter a     novel metabolic state. Genome Biol 2003, 4 (1), R3. -   51. Mittal, N.; Babu, M. M.; Roy, N., The efficiency of     mitochondrial electron transport chain is increased in the     long-lived mrg19 Saccharomyces cerevisiae. Aging Cell 2009, 8 (6),     643-53. -   52. Thomas, M. R.; O'Shea, E. K., An intracellular phosphate buffer     filters transient fluctuations in extracellular phosphate levels.     Proc Natl Acad Sci USA 2005, 102 (27), 9565-70. -   53. Gray, J. V.; Petsko, G. A.; Johnston, G. C.; Ringe, D.;     Singer, R. A.; Wemer-Washburne, M., “Sleeping beauty”: quiescence in     Saccharomyces cerevisiae. Microbiol Mol Biol Rev 2004, 68 (2),     187-206. -   54. Grilly, C.; Stricker, J.; Pang, W. L.; Bennett, M. R.; Hasty,     J., A synthetic gene network for tuning protein degradation in     Saccharomyces cerevisiae. Mol Syst Biol 2007, 3, 127. -   55. Orth, J. D.; Thiele, I.; Palsson, B. O., What is flux balance     analysis? Nat Biotechnol 2010, 28 (3), 245-8. -   56. Yim, H.; Haselbeck, R.; Niu, W.; Pujol-Baxley, C.; Burgard, A.;     Boldt, J.; Khandurina, J.; Trawick, J. D.; Osterhout, R. E.;     Stephen, R.; Estadilla, J.; Teisan, S.; Schreyer, H. B.; Andrae, S.;     Yang, T. H.; Lee, S. Y.; Burk, M. J.; Van Dien, S., Metabolic     engineering of Escherichia coli for direct production of     1,4-butanediol. Nat Chem Biol 2011, 7 (7), 445-52. -   57. Gupta, A.; Reizman, I. M.; Reisch, C. R.; Prather, K. L.,     Dynamic regulation of metabolic flux in engineered bacteria using a     pathway-independent quorum-sensing circuit. Nature biotechnology     2017, 35 (3), 273-279. -   58. Wang, J., Yu, H. Q., Biosynthesis of polyhydroxybutyrate (PHB)     and extracellular polymeric substances (EPS) by Ralstonia eutropha     ATCC 17699 in batch cultures. Appl Microbiol Biotechnol 2007, 75     (4), 871-8. -   59. Xu, P.; Qiao, K.; Ahn, W. S.; Stephanopoulos, G., Engineering     Yarrowia lipolytica as a platform for synthesis of drop-in     transportation fuels and oleochemicals. Proc Natl Acad Sci USA 2016,     113 (39), 10848-53. -   60. Lynch, M. D.; Warnecke, T.; Gill, R. T. Method for Producing     3-Hydroxypropionic Acid and Other Products. Sep. 8, 2011. -   61. Qiao, K.; Wasylenko, T. M.; Zhou, K.; Xu, P.; Stephanopoulos,     G., Lipid production in Yarrowia lipolytica is maximized by     engineering cytosolic redox metabolism. Nat Biotechnol 2017. -   62. Jian, J.; Zhang, S. Q.; Shi, Z. Y.; Wang, W.; Chen, G. Q.; Wu,     Q., Production of polyhydroxyalkanoates by Escherichia coli mutants     with defected mixed acid fermentation pathways. Appl Microbiol     Biotechnol 2010, 87 (6), 2247-56. -   63. Sharan, S. K.; Thomason, L. C.; Kuznetsov, S. G.; Court, D. L.,     Recombineering a homologous recombination-based method of genetic     engineering. Nature protocols 2009, 4 (2), -   206-23. -   64. Li, X. T.; Thomason, L. C.; Sawitzke, J. A.; Costantino, N.;     Court, D. L., Positive and -   negative selection using the tetA-sacB cassette: recombineering and     P1 transduction in Escherichia coli. Nucleic acids research 2013, 41     (22), e204. -   65. Baba, T.; Ara, T.; Hasegawa, M.; Takai, Y.; Okumura, Y.; Baba,     M.; Datsenko, K. A.; Tomita, M.; Wanner, B. L.; Mori, H.,     Construction of Escherichia coli K-12 in-frame, single-gene knockout     mutants: the Keio collection. Molecular systems biology 2006, 2,     2006 0008. -   66. van Dijken, J. P.; Bauer, J.; Brambilla, L.; Duboc, P.;     Francois, J. M.; Gancedo, C.; Giuseppin, M. L. F., Heijnen, J. J.;     Hoare, M.; Lange, H. C.; Madden, E. A.; Niederberger, P.; Nielsen,     J.; Parrou, J. L.; Petit, T.; Porro, D.; Reuss, M.; van Riel, N.;     Rizzi, M.; Steensma, H. Y.; Verrips, C. T.; Vindeløv, J.; Pronk, J.     T., An interlaboratory comparison of physiological and genetic     properties of four Saccharomyces cerevisiae strains. Enzyme and     Microbial Technology 2000, 26 (9-10), 706-714. -   67. Otterstedt, K.; Larsson, C.; Bill, R. M.; Stahlberg, A.; Boles,     E.; Hohmann, S.; Gustafsson, L., Switching the mode of metabolism in     the yeast Saccharomyces cerevisiae. EMBO Rep 2004, 5 (5), 532-7. -   68. Wieczorke, R.; Krampe, S.; Weierstall, T.; Freidel, K.;     Hollenberg, C. P.; Boles, E, Concurrent knock-out of at least 20     transporter genes is required to block uptake of hexoses in     Saccharomyces cerevisiae. FEBS Letters 1999, 464 (3), 123-128. -   69. Gietz, R. D.; Schiestl, R. H., High-efficiency yeast     transformation using the LiAc/SS carrier DNA/PEG method. Nature     protocols 2007, 2 (1), 31-4. -   70. Stovicek, V.; Borodina, I.; Forster, J., CRISPR-Cas system     enables fast and simple genome editing of industrial Saccharomyces     cerevisiae strains. Metabolic Engineering Communications 2015, 2,     13-22. -   71. Labun, K.; Montague, T. G.; Gagnon, J. A.; Thyme, S. B.; Valen,     E., CHOPCHOP v2: a web tool for the next generation of CRISPR genome     engineering. Nucleic acids research 2016, 44 (W1), W272-6. -   72. Hoffman, C. S.; Winston, F., A ten-minute DNA preparation from     yeast efficiently releases autonomous plasmids for transformation of     Escherichia coli. Gene 1987, 57 (2-3), 267-272. -   73. Luo, M. L.; Mullis, A. S.; Leenay, R. T.; Beisel, C. L.,     Repurposing endogenous type I CRISPR-Cas systems for programmable     gene repression. Nucleic acids research 2015, 43 (1), 674-81. -   74. Davis, J. H.; Rubin, A. J.; Sauer, R. T., Design, construction     and characterization of a set of insulated bacterial promoters.     Nucleic acids research 2011, 39 (3), 1131-41. -   75. Smith, J. D.; Suresh, S.; Schlecht, U.; Wu. M.; Wagih, O.;     Peltz, G.; Davis, R. W.; Steinmetz, L. M.; Parts, L.; St Onge, R.     P., Quantitative CRISPR interference screens in yeast identify     chemical-genetic interactions and new rules for guide RNA design.     Genome Biol 2016, 17, 45. -   76. Gilbert, L. A.; Larson, M. H.; Morsut, L.; Liu, Z.; Brar, G. A.;     Torres, S. E.; Stern-Ginossar, N.; Brandman, O.; Whitehead, E. H.;     Doudna, J. A.; Lim, W. A.; Weissman, J. S.; Qi, L. S.,     CRISPR-mediated modular RNA-guided regulation of transcription in     eukaryotes. Cell 2013, 154(2), 442-51. -   77. Sikorski, R. S.; Hieter, P., A system of shuttle vectors and     yeast host strains designed for efficient manipulation of DNA in     Saccharomyces cerevisiae. Genetics 1989, 122 (1), 19-27. -   78. Duetz, W. A.; Ruedi, L.; Hermann, R.; O'Connor, K.; Buchs, J.;     Witholt, B., Methods for intense aeration, growth, storage, and     replication of bacterial strains in microtiter plates. Applied and     environmental microbiology 2000, 66 (6), 2641-6. -   79. Duetz, W. A.; Witholt, B., Effectiveness of orbital shaking for     the aeration of suspended bacterial cultures in square-deepwell     microtiter plates. Biochem Eng J 2001, 7 (2), 113-115. -   80. Lindemann, C. J.; Singh, M. M.; Ramjit, H. G.; Bell, C.; Ip, D.     P., Determination of mevalonolactone in capsules by capillary     gas-liquid chromatography. J Pharm Biomed Anal 1991, 9 (4), 311-6. -   81. Keseler, I. M.; Mackie, A.; Peralta-Gil, M.; Santos-Zavaleta,     A.; Gama-Castro, S.; Bonavides-Martinez, C.; Fulcher, C.; Huerta, A.     M.; Kothari, A.; Krummenacker, M.; Latendresse, M.; Muniz-Rascado,     L.; Ong, Q.; Paley, S.; Schroder, I.; Shearer, A. G.; Subhraveti,     P., Travers, M.; Weerasinghe, D.; Weiss, V.; Collado-Vides, J.;     Gunsalus, R. P.; Paulsen, I.; Karp, P. D., EcoCyc: fusing model     organism databases with systems biology. Nucleic acids research     2013, 41 (Database issue). D605-12. -   82. Davis, J. H.; Rubin, A. J.; Sauer, R. T., Design, construction     and characterization of a set of insulated bacterial promoters.     Nucleic acids research 2011, 39 (3), 1131-41. -   83. Poo, H.; Song, J. J.; Hong, S.-P.; Choi, Y.-H.; Yun, S. W.; Kim,     J.-H.; Lee, S. C.; Lee, S.-G.; Sung, M. H., Novel high-level     constitutive expression system, pHCE vector, for a convenient and     cost-effective soluble production of human tumor necrosis factor-α.     Biotechnology Letters 2002, 24 (14), 1185-1189. -   84. Baba, T.; Ara, T.; Hasegawa, M.; Takai, Y.; Okumura, Y.; Baba,     M.; Datsenko, K. A.; Tomita, M.; Wanner, B. L.; Mori, H.,     Construction of Escherichia coli K-12 in-frame, single-gene knockout     mutants: the Keio collection. Mol Syst Biol 2006, 2, 2006 0008. -   85. Jian, J.; Zhang, S. Q.; Shi, Z. Y.; Wang, W.; Chen, G. Q.; Wu,     Q., Production of polyhydroxyalkanoates by Escherichia coli mutants     with defected mixed acid fermentation pathways. Appl Microbiol     Biotechnol 2010, 87 (6), 2247-56. -   86. van Dijken, J. P., Bauer, J.; Brambilla, L.; Duboc, P;     Francois, J. M.; Gancedo, C.; Giuseppin, M. L. F.; Heijnen, J. J.;     Hoare, M.; Lange, H. C.; Madden, E. A.; Niederberger, P.; Nielsen,     J.; Parrou. J. L.; Petit, T.; Porro, D.; Reuss, M.; van Riel. N.;     Rizzi, M.; Steensma, H. Y.; Verrips, C. T.; Vindeløv, J.; Pronk, J.     T., An interlaboratory comparison of physiological and genetic     properties of four Saccharomyces cerevisiae strains. Enzyme and     Microbial Technology 2000, 26 (9-10), 706-714. -   87. Otterstedt, K.; Larsson, C.; Bill, R. M.; Stahlberg, A.; Boles,     E.; Hohmann, S.; Gustafsson, L., Switching the mode of metabolism in     the yeast Saccharomyces cerevisiae. EMBO Rep 2004, 5 (5), 532-7. -   88. Luo, M. L.; Mullis, A. S.; Leenay, R. T.; Beisel, C. L.,     Repurposing endogenous type I CRISPR-Cas systems for programmable     gene repression. Nucleic acids research 2015, 43 (1), 674-81. -   89. Wilde, R. J.; Guest, J. R., Transcript analysis of the citrate     synthase and succinate dehydrogenase genes of Escherichia coli     K12 J. Gen Microbiol 1986, 132 (12), 3239-51. -   90. Charpentier, B.; Branlant, C., The Escherichia coli gapA gene is     transcribed by the vegetative RNA polymerase holoenzyme E sigma 70     and by the heat shock RNA polymerase E sigma 32. Journal of     Bacteriology 1994, 176 (3), 830-839. -   91. Li, X. T.; Thomason, L. C.; Sawitzke, J. A.; Costantino, N.;     Court, D. L., Positive and negative selection using the tetA-sacB     cassette: recombineering and P1 transduction in Escherichia coli.     Nucleic acids research 2013, 41 (22), e204. -   92. Duetz, W. A.; Ruedi, L.; Hermann, R.; O'Connor, K.; Buchs, J.;     Witholt, B., Methods for intense aeration, growth, storage, and     replication of bacterial strains in microtiter plates. Applied and     environmental microbiology 2000, 66 (6), 2641-6. -   93. Duetz, W. A.; Witholt, B., Effectiveness of orbital shaking for     the aeration of suspended bacterial cultures in square-deepwell     microtiter plates. Biochem Eng J 2001, 7 (2), 113-115.

While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby. 

1.-106. (canceled)
 107. A multi-stage fermentation bioprocess for producing 3-hydroxypropionic acid from a genetically modified microorganism, comprising: providing a genetically modified microorganism for producing 3-hydroxypropionic acid; growing the genetically modified microorganism in a media in a growth phase, the genetically modified microorganism comprising: i. a production pathway comprising at least one production enzyme for biosynthesis of 3-hydroxypropionic acid; and ii. one or more synthetic metabolic valves for reducing or eliminating flux through multiple metabolic pathways within the genetically modified microorganism when the synthetic metabolic valves are induced, the one or more synthetic metabolic valves comprising: a) at least one silencing synthetic metabolic valve that silences gene expression of a gene encoding a silenceable enzyme, or b) at least one proteolytic synthetic metabolic valve that controls proteolysis of a proteolyzable enzyme; transitioning to a productive stationary phase wherein, compared to a microorganism lacking the production pathway or the one or more synthetic metabolic valves, the growth of the genetically modified microorganism is slowed or stopped and 3-hydroxypropionic acid production is enhanced, the productive stationary phase being entered by at least inducing the one or more synthetic metabolic valves; and producing 3-hydroxypropionic acid.
 108. The multi-stage fermentation bioprocess of claim 107, wherein the enzyme of the 3-hydroxypropionic acid production pathway is heterologous to the genetically modified microorganism and the heterologous enzyme is one of: an acetyl-CoA acetyltransferase, NADPH dependent HMG-CoA reductase, HMG-CoA synthase or a combination thereof
 109. The multi-stage fermentation bioprocess of claim 107, wherein the heterologous enzyme of 3-hydroxypropionic acid production is encoded by a gene selected from the group consisting of: E. faecalis mvaE, E. faecalis mvaS, E. faecalis mvaS(A110G), or a combination thereof.
 110. The multi-stage fermentation bioprocess of claim 107, wherein the silenceable enzyme and the proteolyzable enzyme are the same or different, and wherein the silenceable enzyme and the proteolyzable enzyme are each encoded by a gene selected from the group consisting of: fabI, gltA, ldp, zwf, and udhA.
 111. The multi-stage fermentation bioprocess of claim 107, wherein the synthetic metabolic valve comprises controlled proteolysis of fabI or udhA.
 112. The multi-stage fermentation bioprocess of claim 107, wherein the enzymes silenced by the silencing metabolic valve are fabI, gltA1 and gltA2 and the enzymes that are subject to enzyme degradation by the proteolytic metabolic valve are fab I, gltA, and udhA.
 113. The multi-stage fermentation bioprocess of claim 107 wherein at least one silencing synthetic metabolic valve is characterized by CRISPR interference of gene expression and expression of a CASCADE plasmid comprising an array of guide RNA genes.
 114. The multi-stage fermentation bioprocess of claim 107, wherein at least one proteolytic synthetic metabolic valve is characterized by expression of the proteolytic enzyme operably linked to a N-terminal or C-terminal tag and controlled proteolysis by the synthetic metabolic valve is selective for the N-terminal or C-terminal tag upon induction of the proteolytic synthetic metabolic valve.
 115. The multi-stage fermentation bioprocess of claim 107, wherein the genetically modified microorganism comprises a chromosomal modification.
 116. The multi-stage fermentation bioprocess of claim 107, wherein the genetically modified microorganism comprises a chromosomal gene deletion of a gene selected from the group consisting of: lactate dehydrogenase (ldhA), phosphate acetyltransferase (pta), pyruvate oxidase (poxB), pyruvateformate lyase (pflB), methylglyoxal synthase (mgsA), acetate kinase (ackA), alcohol dehydrogenase (adhE), a clpXP protease specificity enhancing factor (sspB), an ATPdependent Lon protease (Ion), an outer membrane protease (ompT), an arcA transcriptional dual regulator (arcA), and an iclR transcriptional regulator (iclR).
 117. The multi-stage fermentation bioprocess of claim 107, wherein the genetically modified microorganism is derived from a genus of bacteria selected from the group consisting of: Citrobacter, Enterobacter, Clostridium, Klebsiella, Aerobacter, Lactobacillus, Aspergillus, Saccharomyces, Schizosaccharomyces, Zygosaccharomyces, Pichia, Kluyveromyces, Candida, Hansenula, Debaryomyces, Mucor, Torulopsis, Methylobacter, Escherichia, Salmonella, Bacillus, Streptomyces, and Pseudomonas.
 118. The multi-stage fermentation bioprocess of claim 107, wherein the genetically modified microorganism is characterized by overexpression of a gene resulting in an increase of a cofactor pool in the genetically modified microorganism during the growth phase.
 119. The multi-stage fermentation bioprocess of claim 107, wherein transitioning to the productive stationary phase is further modulated by at least one of an artificial chemical inducer or depletion of a limiting nutrient from the microorganism culture media.
 120. The multi-stage fermentation bioprocess of claim 107, further comprising altering an environmental factor of the culture media or culture conditions effective to enhance product production, the environmental factor selected from the group consisting of: temperature of the culture media or culture conditions, pH of the culture media, nutrients of the culture media, oxygenation of the culture media, sugar concentration of the culture media, and combinations thereof.
 121. The multi-stage fermentation bioprocess of claim 107, wherein transitioning to the step of entering a productive stationary phase further comprises inducing expression of the enzyme of the production pathway.
 122. The multi-stage fermentation bioprocess of claim 107, wherein the genetically modified microorganism produces at least 0.5 grams of 3-hydroxypropionic acid per liter per hour.
 123. A genetically modified microorganism, comprising: i. a production pathway comprising at least one production enzyme for biosynthesis of 3-hydroxypropionic acid; and ii. one or more synthetic metabolic valves for reducing or eliminating flux through multiple metabolic pathways within the genetically modified microorganism when the one or more synthetic metabolic valves are induced, the one or more synthetic metabolic valves comprising: a) at least one silencing synthetic metabolic valve that silences gene expression of a gene encoding one or more of the enzymes: fabI, gltA1 or gltA2, or b) at least one proteolytic synthetic metabolic valve that controls proteolysis of one or more of the proteolyzable enzymes: fab I, gltA, or udhA; wherein growth of the genetically modified microorganism is slowed or stopped and 3-hydroxypropionic acid production is enhanced, as compared to a microorganism lacking the production pathway or synthetic metabolic valve, by inducing the synthetic metabolic valve.
 124. The genetically modified microorganism of claim 123, wherein the enzyme of the 3-hydroxypropionic acid production pathway is heterologous to the GMO and is selected from the group consisting of: E. faecalis mvaE, E. faecalis mvaS, E. faecalis mvaS(A110G), or a combination thereof.
 125. The genetically modified microorganism of claim 123, wherein the proteolytic synthetic metabolic valve controls proteolysis of fabI or udhA.
 126. The genetically modified microorganism of claim 123, wherein the genetically modified microorganism in an E. coli microorganism. 